Neural network based speech synthesizer: A preliminary report

A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan

A space-time neural network

Introduced here is a novel technique which adds the dimension of time to the well known back propagation neural network algorithm. Cited here are several reasons why the inclusion of automated spatial and temporal associations are crucial to effective systems modeling. An overview of other works which also model spatiotemporal dynamics is furnished. A detailed description is given of the processes necessary to implement the space-time network algorithm. Several demonstrations that illustrate the capabilities and performance of this new architecture are given

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies

Space time neural networks for tether operations in space

A space shuttle flight scheduled for 1992 will attempt to prove the feasibility of operating tethered payloads in earth orbit. due to the interaction between the Earth's magnetic field and current pulsing through the tether, the tethered system may exhibit a circular transverse oscillation referred to as the 'skiprope' phenomenon. Effective damping of skiprope motion depends on rapid and accurate detection of skiprope magnitude and phase. Because of non-linear dynamic coupling, the satellite attitude behavior has characteristic oscillations during the skiprope motion. Since the satellite attitude motion has many other perturbations, the relationship between the skiprope parameters and attitude time history is very involved and non-linear. We propose a Space-Time Neural Network implementation for filtering satellite rate gyro data to rapidly detect and predict skiprope magnitude and phase. Training and testing of the skiprope detection system will be performed using a validated Orbital Operations Simulator and Space-Time Neural Network software developed in the Software Technology Branch at NASA's Lyndon B. Johnson Space Center

Learning characteristics of a space-time neural network as a tether skiprope observer

The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table

Neural network for processing both spatial and temporal data with time based back-propagation

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

Prototyping a Conductive Polymer Steering Pad for Rail Freight Service

The AdapterPlus™ steering pad is a polymer component on a railcar that helps to reduce stresses on the axle as a railcar rounds a curve. One railway application requires a minimum of 240 mA to be passed through the steering pad to the rail, which activates air valves that control automated cargo gates. Currently, two copper studs are inserted into the pad to provide a conductive path. However, after continuous cyclic loading caused by normal service operation, the copper studs deform, wear, and eventually lose contact between the two surfaces rendering the pad nonconductive. One proposed solution to this problem is to create a steering pad made entirely from an electrically conductive material. The University Transportation Center for Railway Safety (UTCRS) research team has successfully created a conductive nanocomposite made from vapor grown carbon nanofibers (CNFs) and a modified form of Elastollan 1195A thermoplastic polyurethane (TPU). Previous attempts to create this material were promising but failed to produce an electrically conductive specimen when injection molded. Preliminary results have shown that the new material can be injection molded to create an electrically conductive test specimen. An injection molded insert was designed, fabricated, and incorporated into the existing steering pad design for further testing. Pressure measurement film had previously been used to find the points of maximum stress inside the pad to optimize the design of the composite insert. Characterization of the resistivity of the composite material was carried out in order to verify functionality in future iterations of this product. The resistance of the composite material is expected to be non-linear with a strong dependence on load and voltage. Conductivity tests were performed using a material testing system with a compressive load ranging from 1500 pounds to 5500 pounds. The voltage at each load was also varied between 10V to 20V and the nonlinear resistance of the material was examined. The results have shown that the CNF/TPU composite is a potential replacement for the current TPU used for the pad and, with minimal modifications, can be implemented in field service operation

Metabotyping the Welsh population of badgers based on thoracic fluid

INTRODUCTION: The European badger (Meles meles) is a known wildlife reservoir for bovine tuberculosis (bTB) and a better understanding of the epidemiology of bTB in this wildlife species is required for disease control in both wild and farmed animals. Flow infusion electrospray—high-resolution mass spectrometry (FIE-HRMS) may potentially identify novel metabolite biomarkers based on which new, rapid, and sensitive point of care tests for bTB infection could be developed. OBJECTIVES: In this foundational study, we engaged on assessing the baseline metabolomic variation in the non-bTB infected badger population (“metabotyping”) across Wales. METHODS: FIE-HRMS was applied on thoracic fluid samples obtained by post-mortem of bTB negative badgers (n = 285) which were part of the Welsh Government ‘All Wales Badger Found Dead’ study. RESULTS: Using principal component analysis and partial least squares—discriminant analyses, the major sources of variation were linked to sex, and to a much lesser extent age, as indicated by tooth wear. Within the female population, variation was seen between lactating and non-lactating individuals. No significant variation linked to the presence of bite wounds, obvious lymphatic lesions or geographical region of origin was observed. CONCLUSION: Future metabolomic work when making comparisons between bTB infected and non-infected badger samples will only need be sex-matched and could focus on males only, to avoid lactation bias. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-022-01888-6