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Two different signal processing algorithms are described for detection and classification of acoustic signals generated by firearm discharges in small enclosed spaces. The first is based on the logarithm of the signal energy. The second is a joint entropy. The current study indicates that a system using both signal energy and joint entropy would be able to both detect weapon discharges and classify weapon type, in small spaces, with high statistical certainty.

Previous results on the use of joint entropy for detection of targeted nanoparticles accumulating in the neovasculature of MDA435 tumors [Fig. 7 of M. S. Hughes et al., J. Acoust. Soc. Am. 133, 283-300 (2013)] are extended, with sensitivity improving by nearly another factor of 2. This result is obtained using a "quasi-optimal" reference waveform in the computation of the joint entropy imaging technique used to image the accumulating nanoparticles.

This study is based on an extension of the concept of joint entropy of two random variables to continuous functions, such as backscattered ultrasound. For two continuous random variables, X and Y, the joint probability density p(x,y) is ordinarily a continuous function of x and y that takes on values in a two dimensional region of the real plane. However, in the case where X=f(t) and Y=g(t) are both continuously differentiable functions, X and Y are concentrated exclusively on a curve, γ(t)=(f(t),g(t)), in the x,y plane. This concentration can only be represented using a mathematically "singular" object such as a (Schwartz) distribution. Its use for imaging requires a coarse-graining operation, which is described in this study. Subsequently, removal of the coarse-graining parameter is accomplished using the ergodic theorem. The resulting expression for joint entropy is applied to several data sets, showing the utility of the concept for both materials characterization and detection of targeted liquid nanoparticle ultrasonic contrast agents. In all cases, the sensitivity of these techniques matches or exceeds, sometimes by a factor of two, that demonstrated in previous studies that employed signal energy or alternate entropic quantities.

Previously a new method for ultrasound signal characterization using entropy H(f) was reported, and it was demonstrated that in certain settings, further improvements in signal characterization could be obtained by generalizing to Renyi entropy-based signal characterization I(f)(r) with values of r near 2 (specifically r=1.99) [M. S. Hughes et al., J. Acoust. Soc. Am. 125, 3141-3145 (2009)]. It was speculated that further improvements in sensitivity might be realized at the limit r-->2. At that time, such investigation was not feasible due to excessive computational time required to calculate I(f)(r) near this limit. In this paper, an asymptotic expression for the limiting behavior of I(f)(r) as r-->2 is derived and used to present results analogous to those obtained with I(f)(1.99). Moreover, the limiting form I(f,infinity) is computable directly from the experimentally measured waveform f(t) by an algorithm that is suitable for real-time calculation and implementation.

Previous work has demonstrated that a signal receiver based on a limiting form of the Shannon entropy is, in certain settings, more sensitive to subtle changes in scattering architecture than conventional energy-based signal receivers [M. S. Hughes et al., J. Acoust. Soc. Am. 121, 3542-3557 (2007)]. In this paper new results are presented demonstrating further improvements in sensitivity using a signal receiver based on the Renyi entropy.

Qualitative and quantitative properties of the finite part, H(f), of the Shannon entropy of a continuous waveform f(t) in the continuum limit are derived in order to illuminate its use for waveform characterization. Simple upper and lower bounds on H(f), based on features of f(t), are defined. Quantitative criteria for a priori estimation of the average-case variation of H(f) and log E(f), where E(f) is the signal energy of f(t) are also derived. These provide relative sensitivity estimates that could be used to prospectively choose optimal imaging strategies in real-time ultrasonic imaging machines, where system bandwidth is often pushed to its limits. To demonstrate the utility of these sensitivity relations for this application, a study designed to assess the feasibility of identification of angiogenic neovasculature targeted with perfluorocarbon nanoparticles that specifically bind to alpha(v)beta3-integrin expression in tumors was performed. The outcome of this study agrees with the prospective sensitivity estimates that were used for the two receivers. Moreover, these data demonstrate the ability of entropy-based signal receivers when used in conjunction with targeted nanoparticles to elucidate the presence of alpha(v)beta3 integrins in primordial neovasculature, particularly in acoustically unfavorable environments.