Development of a Fast-Running Algorithm to Approximate Incident Blast Parameters Using Body-Mounted Sensor Measurements

ABSTRACT Introduction The Office of Naval Research sponsored the Blast Load Assessment-Sense and Test program to develop a rapid, in-field solution that could be used by team leaders, commanders, and medical personnel to make science-based stand-down decisions for service members exposed to blast overpressure. However, a critical challenge to this goal was the reliable interpretation of surface pressure data collected by body-worn blast sensors in both combat and combat training scenarios. Without an appropriate standardized metric, exposures from different blast events cannot be compared and accumulated in a service member’s unique blast exposure profile. In response to these challenges, we developed the Fast Automated Signal Transformation, or FAST, algorithm to automate the processing of large amounts of pressure–time data collected by blast sensors and provide a rapid, reliable approximation of the incident blast parameters without user intervention. This paper describes the performance of the FAST algorithms developed to approximate incident blast metrics from high-explosive sources using only data from body-mounted blast sensors. Methods and Materials Incident pressure was chosen as the standardized output metric because it provides a physiologically relevant estimate of the exposure to blast that can be compared across multiple events. In addition, incident pressure serves as an ideal metric because it is not directionally dependent or affected by the orientation of the operator. The FAST algorithms also preprocess data and automatically flag “not real” traces that might not be from blasts events (false positives). Elimination of any “not real” blast waveforms is essential to avoid skewing the results of subsequent analyses. To evaluate the performance of the FAST algorithms, the FAST results were compared to (1) experimentally measured pressures and (2) results from high-fidelity numerical simulations for three representative real-world events. Results The FAST results were in good agreement with both experimental data and high-fidelity simulations for the three case studies analyzed. The first case study evaluated the performance of FAST with respect to body shielding. The predicted incident pressure by FAST for a surrogate facing the charge, side on to charge, and facing away from the charge was examined. The second case study evaluated the performance of FAST with respect to an irregular charge compared to both pressure probes and results from high-fidelity simulations. The third case study demonstrated the utility of FAST for detonations inside structures where reflections from nearby surfaces can significantly alter the incident pressure. Overall, FAST predictions accounted for the reflections, providing a pressure estimate typically within 20% of the anticipated value. Conclusions This paper presents a standardized approach—the FAST algorithms—to analyze body-mounted blast sensor data. FAST algorithms account for the effects of shock interactions with the body to produce an estimate of incident blast conditions, allowing for direct comparison of individual exposure from different blast events. The continuing development of FAST algorithms will include heavy weapons, providing a singular capability to rapidly interpret body-worn sensor data, and provide standard output for analysis of an individual’s unique blast exposure profile.

Node Generation for RBF-FD Methods by QR Factorization

Polyharmonic spline (PHS) radial basis functions (RBFs) have been used in conjunction with polynomials to create RBF finite-difference (RBF-FD) methods. In 2D, these methods are usually implemented with Cartesian nodes, hexagonal nodes, or most commonly, quasi-uniformly distributed nodes generated through fast algorithms. We explore novel strategies for computing the placement of sampling points for RBF-FD methods in both 1D and 2D while investigating the benefits of using these points. The optimality of sampling points is determined by a novel piecewise-defined Lebesgue constant. Points are then sampled by modifying a simple, robust, column-pivoting QR algorithm previously implemented to find sets of near-optimal sampling points for polynomial approximation. Using the newly computed sampling points for these methods preserves accuracy while reducing computational costs by mitigating stencil size restrictions for RBF-FD methods. The novel algorithm can also be used to select boundary points to be used in conjunction with fast algorithms that provide quasi-uniformly distributed nodes.

Fast Algorithms for Relational Marginal Polytopes

We study the problem of constructing the relational marginal polytope (RMP) of a given set of first-order formulas. Past work has shown that the RMP construction problem can be reduced to weighted first-order model counting (WFOMC). However, existing reductions in the literature are intractable in practice, since they typically require an infeasibly large number of calls to a WFOMC oracle. In this paper, we propose an algorithm to construct RMPs using fewer oracle calls. As an application, we also show how to apply this new algorithm to improve an existing approximation scheme for WFOMC. We demonstrate the efficiency of the proposed approaches experimentally, and find that our method provides speed-ups over the baseline for RMP construction of a full order of magnitude.

Towards New Multiwavelets: Associated Filters and Algorithms. Part I: Theoretical Framework and Investigation of Biomedical Signals, ECG and Coronavirus Cases

Biosignals are nowadays important subjects for scientific researches from both theory, and applications, especially, with the appearance of new pandemics threatening the humanity such as the new Coronavirus. One aim in the present work is to prove that Wavelets may be a successful machinery to understand such phenomena by applying a step forward extension of wavelets to multi-wavelets. We proposed in a first step to improve multi-wavelet notion by constructing more general families using independent components for multi-scaling, and multi-wavelet mother functions. A special multi-wavelet is then introduced, continuous, and discrete multi-wavelet transforms are associated, as well as new filters, and algorithms of decomposition, and reconstruction. The constructed multi-wavelet framework is applied for some experimentations showing fast algorithms, ECG signal, and a strain of Coronavirus processing.

Fast verification for the Perron pair of an irreducible nonnegative matrix

Fast algorithms are proposed for calculating error bounds for a numerically computed Perron root and vector of an irreducible nonnegative matrix. Emphasis is put on the computational efficiency of these algorithms. Error bounds for the root and vector are based on the Collatz--Wielandt theorem, and estimating a solution of a linear system whose coefficient matrix is an $M$-matrix, respectively. We introduce a technique for obtaining better error bounds. Numerical results show properties of the algorithms.