Acoustical evaluation of the NASA Lewis 9 by 15 foot low speed wind tunnel

The test section of the NASA Lewis 9- by 15-Foot Low Speed Wind Tunnel was acoustically treated to allow the measurement of acoustic sources located within the tunnel test section under simulated free field conditions. The treatment was designed for high sound absorption at frequencies above 250 Hz and to withstand tunnel airflow velocities up to 0.2 Mach. Evaluation tests with no tunnel airflow were conducted in the test section to assess the performance of the installed treatment. This performance would not be significantly affected by low speed airflow. Time delay spectrometry tests showed that interference ripples in the incident signal resulting from reflections occurring within the test section average from 1.7 dB to 3.2 dB wide over a 500 to 5150 Hz frequency range. Late reflections, from upstream and downstream of the test section, were found to be insignificant at the microphone measuring points. For acoustic sources with low directivity characteristics, decay with distance measurements in the test section showed that incident free field behavior can be measured on average with an accuracy of +/- 1.5 dB or better at source frequencies from 400 Hz to 10 kHz. The free field variations are typically much smaller with an omnidirectional source

Comparison between design and installed acoustic characteristics of NASA Lewis 9- by 15-foot low-speed wind tunnel acoustic treatment

The test section of the NASA Lewis 9- by 15-Foot Low-Speed Wind Tunnel was acoustically treated to allow the measurement of sound under simulated free-field conditions. The treatment was designed for high sound absorption at frequencies above 250 Hz and for withstanding the environmental conditions in the test section. In order to achieve the design requirements, a fibrous, bulk-absorber material was packed into removable panel sections. Each section was divided into two equal-depth layers packed with material to different bulk densities. The lower density was next to the facing of the treatment. The facing consisted of a perforated plate and screening material layered together. Sample tests for normal-incidence acoustic absorption were also conducted in an impedance tube to provide data to aid in the treatment design. Tests with no airflow, involving the measurement of the absorptive properties of the treatment installed in the 9- by 15-foot wind tunnel test section, combined the use of time-delay spectrometry with a previously established free-field measurement method. This new application of time-delay spectrometry enabled these free-field measurements to be made in nonanechoic conditions. The results showed that the installed acoustic treatment had absorption coefficients greater than 0.95 over the frequency range 250 Hz to 4 kHz. The measurements in the wind tunnel were in good agreement with both the analytical prediction and the impedance tube test data

Excitable Scale Free Networks

When a simple excitable system is continuously stimulated by a Poissonian external source, the response function (mean activity versus stimulus rate) generally shows a linear saturating shape. This is experimentally verified in some classes of sensory neurons, which accordingly present a small dynamic range (defined as the interval of stimulus intensity which can be appropriately coded by the mean activity of the excitable element), usually about one or two decades only. The brain, on the other hand, can handle a significantly broader range of stimulus intensity, and a collective phenomenon involving the interaction among excitable neurons has been suggested to account for the enhancement of the dynamic range. Since the role of the pattern of such interactions is still unclear, here we investigate the performance of a scale-free (SF) network topology in this dynamic range problem. Specifically, we study the transfer function of disordered SF networks of excitable Greenberg-Hastings cellular automata. We observe that the dynamic range is maximum when the coupling among the elements is critical, corroborating a general reasoning recently proposed. Although the maximum dynamic range yielded by general SF networks is slightly worse than that of random networks, for special SF networks which lack loops the enhancement of the dynamic range can be dramatic, reaching nearly five decades. In order to understand the role of loops on the transfer function we propose a simple model in which the density of loops in the network can be gradually increased, and show that this is accompanied by a gradual decrease of dynamic range.Comment: 6 pages, 4 figure

Subgraphs and network motifs in geometric networks

Many real-world networks describe systems in which interactions decay with the distance between nodes. Examples include systems constrained in real space such as transportation and communication networks, as well as systems constrained in abstract spaces such as multivariate biological or economic datasets and models of social networks. These networks often display network motifs: subgraphs that recur in the network much more often than in randomized networks. To understand the origin of the network motifs in these networks, it is important to study the subgraphs and network motifs that arise solely from geometric constraints. To address this, we analyze geometric network models, in which nodes are arranged on a lattice and edges are formed with a probability that decays with the distance between nodes. We present analytical solutions for the numbers of all 3 and 4-node subgraphs, in both directed and non-directed geometric networks. We also analyze geometric networks with arbitrary degree sequences, and models with a field that biases for directed edges in one direction. Scaling rules for scaling of subgraph numbers with system size, lattice dimension and interaction range are given. Several invariant measures are found, such as the ratio of feedback and feed-forward loops, which do not depend on system size, dimension or connectivity function. We find that network motifs in many real-world networks, including social networks and neuronal networks, are not captured solely by these geometric models. This is in line with recent evidence that biological network motifs were selected as basic circuit elements with defined information-processing functions.Comment: 9 pages, 6 figure

Evaluation of Few-View Reconstruction Parameters for Illicit Substance Detection using Fast-Neutron Transmission Spectroscopy

We have evaluated the performance of an illicit substance detection system that performs image reconstruction using the Maximum Likelihood algebraic reconstruction algorithm, a few number of projections, and relatively coarse projection and pixel resolution. This evaluation was done using receiver operator curves and simulated data from the fast-neutron transmission spectroscopy system operated in a mode to detect explosives in luggage. The results show that increasing the number of projection angles is more important than increasing the projection resolution, the reconstructed pixel resolution, or the number of iterations in the Maximum Likelihood algorithm. A 100% detection efficiency with essentially no false positives is possible for a square block of RDX explosive, a projection resolution of 2 cm, a reconstructed pixel size of 2x2 cm, and five projection angles. For rectangular shaped explosives more angles are required to obtain the same system performance

A motif-based approach to network epidemics

Networks have become an indispensable tool in modelling infectious diseases, with the structure of epidemiologically relevant contacts known to affect both the dynamics of the infection process and the efficacy of intervention strategies. One of the key reasons for this is the presence of clustering in contact networks, which is typically analysed in terms of prevalence of triangles in the network. We present a more general approach, based on the prevalence of different four-motifs, in the context of ODE approximations to network dynamics. This is shown to outperform existing models for a range of small world networks

Evaluating Local Community Methods in Networks

We present a new benchmarking procedure that is unambiguous and specific to local community-finding methods, allowing one to compare the accuracy of various methods. We apply this to new and existing algorithms. A simple class of synthetic benchmark networks is also developed, capable of testing properties specific to these local methods.Comment: 8 pages, 9 figures, code included with sourc

Statistical significance of rich-club phenomena in complex networks

We propose that the rich-club phenomena in complex networks should be defined in the spirit of bootstrapping, in which a null model is adopted to assess the statistical significance of the rich-club detected. Our method can be served as a definition of rich-club phenomenon and is applied to analyzing three real networks and three model networks. The results improve significantly compared with previously reported results. We report a dilemma with an exceptional example, showing that there does not exist an omnipotent definition for the rich-club phenomenon.Comment: 3 Revtex pages + 5 figure