Mariner IV science platform structure and actuator design, development and flight performance

Mariner IV scan platform structure and actuator design - development and performanc

Pregnanediol Excretion in Normal and Abnormal Pregnancy

Abstract Not Provided

Preliminary assessment of industrial needs for an advanced ocean technology

A quick-look review of selected ocean industries is presented for the purpose of providing NASA OSTA with an assessment of technology needs and market potential. The size and growth potential, needs and problem areas, technology presently used and its suppliers, are given for industries involved in deep ocean mining, petrochemicals ocean energy conversion. Supporting services such as ocean bottom surveying; underwater transportation, data collection, and work systems; and inspection and diving services are included. Examples of key problem areas that are amenable to advanced technology solutions are included. Major companies are listed

Finite Memory Recursive Solutions in Stochastic Models: Equilibrium and Transient Analysis

G/M/1 and M/G/1-type Markov processes provide natural models for widely differing stochastic phenomena. Efficient recursive solutions for the equilibrium and transient analysis of these processes are therefore of considerable interest. In this direction, a new class of recursive solutions are proposed for the analysis of M/G/l and G/M/l type processes. In this report, the notion of when a process is LEDI-complete, which means it has complete Level Entrance Direction Information, is introduced for G/M/1-type Markov processes. This notion leads to a new class of recursive solutions, called finite-memory recursive solutions, for the equilibrium probabilities of a class of G/M/ 1-type Markov processes. A finite-memory recursive solution of order k has the form πn+k = π W1 +π n+1W2 + ••• +πn+k-1 Wk7 where πn is the vector of limiting probabilities of the states on level n of the process and Wi, 1 \u3c i \u3c k, are square matrices. It is also shown that the concept of LEDI- completeness leads to a finite- memory recursive solution for the transient behavior of this class of G/M/-1- type processes. Such a recursive solution has the form πn+k(s) = ^n(s)W1(s) +π n+i(s)W2(s) + • • • + πn+k-i(s)Wk(s). where π(s) is the Laplace transform of πn(t), the vector of state occupancy probabilities at time t for the states on level n of the process. The relationship between these finite-memory recursive solutions and matrix geometric solutions is also explored. The results are extended to the case where the transition rates are level dependent. It is also briefly explained how a finite memory recursion for the equilibrium and transient probabilities of M/G/l type Markov processes can be obtained

EEG-Based Communication:A Time Series Prediction Approach

Recently, a new technology known as the braincomputer interface (BCI) has received a substantial amount of interest among various research groups worldwide. The human brain can be represented by self-organising and complex biochemical states. Due to continuous neuronal activity in the brain, chaotic electric potential waves are observed in Electroencephalogram (EEG) recordings of the brain. A BCI involves extracting information from the highly complex EEG. This is achieved by obtaining the dominant discriminating features from different EEG signals recorded during specific thought processes. A class of features is usually obtained from each thought process and subsequently a classifier is trained to learn which feature belongs to which class. This ultimately leads to a system that can determine which thoughts belong to which set of EEG signals. This work outlines a novel method which utilises cybernetic intelligence in the form of Neural Networks (NN). Three NNs are coalesced to perform simplified simulations of a number of the characteristic and complex processes that are sub-consciously performed in the human brain. These include prediction, feature extraction and classification. These processes are combined in this system to produce a pattern recognition system which distinguishes between similar complex patterns from a noisy environment with classification accuracy which compares satisfactorily to current reported results. The classification accuracy is achieved by increasing the separability between the features extracted from two EEG signals recorded from subjects during imagination of left and right arm movement