Naval Surface Warfare Center

About: Naval Surface Warfare Center is a facility organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Sonar & Radar. The organization has 2855 authors who have published 3697 publications receiving 83518 citations. The organization is also known as: NSWC.

Neural network adaptive wavelets for signal representation and classification

Abstract: Methods are presented for adaptively generating wavelet templates for signal representation and classification using neural networks. Different network structures and energy functions are necessary and are given for representation and classification. The idea is introduced of a "super-wavelet," a linear combination of wavelets that itself is treated as a wavelet. The super-wavelet allows the shape of the wavelet to adapt to a particular problem, which goes beyond adapting parameters of a fixed-shape wavelet. Simulations are given for 1-D signals, with the concepts extendable to imagery. Ideas are discussed for applying the concepts in the paper to phoneme and speaker recognition.

Empirical Spectral Model of Surface Pressure Fluctuations

Abstract: An empirical model of the surface pressure spectrum beneath a two-dimensional, zero-pressure-gradient boundary layer is presented that is based on the experimental surface pressure spectra measured by seven research groups. The measurements cover a large range of Reynolds number, 1.4 × 10 3 < Reθ < 2.34 × × 10 4 . The model is a simple function of the ratio of the timescales of the outer to inner boundary layer. It incorporates the effect of Reynolds number through the timescale ratio and compares well to experimental data. It is proposed that the effect of Reynolds number is more aptly described as the effect of the range of relevant scales. Spectral features of the experimental data and the scaling behavior of the surface pressure spectrum are also discussed.

Energetic salts with π-stacking and hydrogen-bonding interactions lead the way to future energetic materials.

Abstract: Among energetic materials, there are two significant challenges facing researchers: 1) to develop ionic CHNO explosives with higher densities than their parent nonionic molecules and (2) to achieve a fine balance between high detonation performance and low sensitivity. We report a surprising energetic salt, hydroxylammonium 3-dinitromethanide-1,2,4-triazolone, that exhibits exceptional properties, viz., higher density, superior detonation performance, and improved thermal, impact, and friction stabilities, then those of its precursor, 3-dinitromethyl-1,2,4-triazolone. The solid-state structure features of the new energetic salt were investigated with X-ray diffraction which showed π-stacking and hydrogen-bonding interactions that contribute to closer packing and higher density. According to the experimental results and theoretical analysis, the newly designed energetic salt also gives rise to a workable compromise in high detonation properties and desirable stabilities. These findings will enhance the future prospects for rational energetic materials design and commence a new chapter in this field.

Stochastic Resonance in a Neuronal Network from Mammalian Brain

Abstract: Stochastic resonance, a nonlinear phenomenon in which random noise optimizes a system’s response to a signal, has been postulated to provide a role for noise in information processing in the brain. In these experiments, a time varying electric field was used to deliver both signal and noise directly to a network of neurons from mammalian brain. As the magnitude of the stochastic component of the field was increased, resonance was observed in the response of the neuronal network to a weak periodic signal. This is the first demonstration of stochastic resonance in neuronal networks from the brain. [S0031-9007(96)01583-9]