Novel convolution-based signal processing techniques for an artificial olfactory mucosa

As our understanding of the human olfactory system has grown, so has our ability to design artificial devices that mimic its functionality, so called electronic noses (e-noses). This has led to the development of a more sophisticated biomimetic system known as an artificial olfactory mucosa (e-mucosa) that comprises a large distributed sensor array and artificial mucous layer. In order to exploit fully this new architecture, new approaches are required to analyzing the rich data sets that it generates. In this paper, we propose a novel convolution based approach to processing signals from the e-mucosa. Computer simulations are performed to investigate the robustness of this approach when subjected to different real-world problems, such as sensor drift and noise. Our results demonstrate a promising ability to classify odors from poor sensor signals

Energy Efficient Engine Program: Technology Benefit/Cost Study, Volume II

The Benefit/Cost Study portion of the NASA-sponsored Energy Efficient Engine Component Development and Integration program was successful in achieving its objectives: identification of air transport propulsion system technology requirements for the years 2000 and 2010, and formulation of programs for developing these technologies. It is projected that the advanced technologies identified, when developed to a state of readiness, will provide future commercial and military turbofan engines with significant savings in fuel consumption and related operating costs. These benefits are significant and far from exhausted. The potential savings translate into billions of dollars in annual savings for the airlines. Analyses indicate that a significant portion of the overall savings is attributed to aerodynamic and structure advancements. Another important consideration in acquiring these benefits is developing a viable reference technology base that will permit engines to operate at substantially higher overall pressure ratios and bypass ratios. Results have pointed the direction for future research and a comprehensive program plan for achieving this was formulated. The next major step is initiating the program effort that will convert the advanced technologies into the expected benefits

Space Transportation Materials and Structures Technology Workshop

The Space Transportation Materials and Structures Technology Workshop was held on September 23-26, 1991, in Newport News, Virginia. The workshop, sponsored by the NASA Office of Space Flight and the NASA Office of Aeronautics and Space Technology, was held to provide a forum for communication within the space materials and structures technology developer and user communities. Workshop participants were organized into a Vehicle Technology Requirements session and three working panels: Materials and Structures Technologies for Vehicle Systems, Propulsion Systems, and Entry Systems

Space Transportation Materials and Structures Technology Workshop. Volume 1: Executive summary

The workshop was held to provide a forum for communication within the space materials and structures technology developer and user communities. Workshop participants were organized into a Vehicle Technology Requirements session and three working panels: Materials and Structures Technologies for Vehicle Systems; Propulsion Systems; and Entry Systems. The goals accomplished were (1) to develop important strategic planning information necessary to transition materials and structures technologies from lab research programs into robust and affordable operational systems; (2) to provide a forum for the exchange of information and ideas between technology developers and users; and (3) to provide senior NASA management with a review of current space transportation programs, related subjects, and specific technology needs. The workshop thus provided a foundation on which a NASA and industry effort to address space transportation materials and structures technologies can grow

Correlations between hidden units in multilayer neural networks and replica symmetry breaking

We consider feed-forward neural networks with one hidden layer, tree architecture and a fixed hidden-to-output Boolean function. Focusing on the saturation limit of the storage problem the influence of replica symmetry breaking on the distribution of local fields at the hidden units is investigated. These field distributions determine the probability for finding a specific activation pattern of the hidden units as well as the corresponding correlation coefficients and therefore quantify the division of labor among the hidden units. We find that although modifying the storage capacity and the distribution of local fields markedly replica symmetry breaking has only a minor effect on the correlation coefficients. Detailed numerical results are provided for the PARITY, COMMITTEE and AND machines with K=3 hidden units and nonoverlapping receptive fields.Comment: 9 pages, 3 figures, RevTex, accepted for publication in Phys. Rev.

Statistical mechanics of the multi-constraint continuous knapsack problem

We apply the replica analysis established by Gardner to the multi-constraint continuous knapsack problem,which is one of the linear programming problems and a most fundamental problem in the field of operations research (OR). For a large problem size, we analyse the space of solution and its volume, and estimate the optimal number of items to go into the knapsack as a function of the number of constraints. We study the stability of the replica symmetric (RS) solution and find that the RS calculation cannot estimate the optimal number of items in knapsack correctly if many constraints are required.In order to obtain a consistent solution in the RS region,we try the zero entropy approximation for this continuous solution space and get a stable solution within the RS ansatz.On the other hand, in replica symmetry breaking (RSB) region, the one step RSB solution is found by Parisi's scheme. It turns out that this problem is closely related to the problem of optimal storage capacity and of generalization by maximum stability rule of a spherical perceptron.Comment: Latex 13 pages using IOP style file, 5 figure

Derivation of Hebb's rule

On the basis of the general form for the energy needed to adapt the connection strengths of a network in which learning takes place, a local learning rule is found for the changes of the weights. This biologically realizable learning rule turns out to comply with Hebb's neuro-physiological postulate, but is not of the form of any of the learning rules proposed in the literature. It is shown that, if a finite set of the same patterns is presented over and over again to the network, the weights of the synapses converge to finite values. Furthermore, it is proved that the final values found in this biologically realizable limit are the same as those found via a mathematical approach to the problem of finding the weights of a partially connected neural network that can store a collection of patterns. The mathematical solution is obtained via a modified version of the so-called method of the pseudo-inverse, and has the inverse of a reduced correlation matrix, rather than the usual correlation matrix, as its basic ingredient. Thus, a biological network might realize the final results of the mathematician by the energetically economic rule for the adaption of the synapses found in this article.Comment: 29 pages, LaTeX, 3 figure

Quantitative Evaluation of NDE Reliability

A comprehensive reliability programme is being performed by the UKAEA and CEGB which is studying aspects such as inspection procedures, equipment, and data interpretation and reporting(1). The influence of management and organisational aspects, and psychological and environmental factors are also being investigated, and the importance of these aspects has recently been highlighted by Behravesh et al(2). The information produced will enable recommendations to be made on methods of eliminating or controlling potential errors. These recommendations should, when coupled with a demonstration of the capability of the procedures, lead to objective and auditable assurance of the overall reliability of the inspection

Analytical and Numerical Study of Internal Representations in Multilayer Neural Networks with Binary Weights

We study the weight space structure of the parity machine with binary weights by deriving the distribution of volumes associated to the internal representations of the learning examples. The learning behaviour and the symmetry breaking transition are analyzed and the results are found to be in very good agreement with extended numerical simulations.Comment: revtex, 20 pages + 9 figures, to appear in Phys. Rev.