With the rapid proliferation of Machine-Learning (ML) and Deep Learning (DL) based decision systems, properly characterizing their often unpredictable performance is a key challenge. In this work we introduce the notion of a Sequential Data-Driven Decision Function (S-D3F), as a data-driven analogue to the Sequential Probability Ratio Test (SPRT). Key performance metrics for sequential analysis are shown suitable for use in analyzing the S-D3F’s performance both in terms of error probabilities and average stopping times. The notion of rate function from large deviations theory is extended to this S-D3F test, and it is shown that with a sequential approach the S-D3F can outperform its Fixed Sample-Size (FSS) counterpart in the D3F as the average number of samples needed to make a decision diverges.

This paper presents a mechanism for evaluating the Root Mean Square Error (RMSE) of a Minimum Variance Unbiased Estimator (MVUE) of a target state in 3D space using acoustic measurements. The target state is represented by $(\theta, \phi, r)$ and it is estimated using Time Difference of Arrival measurements at the sensors and we assume that the sound-speed c is unknown. We then examine the interaction between azimuth angle $\theta$ on range RMSE, and the impacts of measurement noise variance on RMSE of $(\theta, \phi, r, c)$ estimates. These results and analytical formulations can be used as a baseline to evaluate proper 3D array geometry design, as well as inform the potential RMSE improvements when using a biased minimum mean square error (MMSE) estimator over an unbiased (MVUE) one for the same set of measurements.

This paper develops computationally efficient algorithms for the analysis of acoustic data to localize a target through improved angle of arrival estimation. The passive target localization problem has a wide range of applications in wireless communication, navigation, acoustic sensor networks, indoor localization, to name a few. We have focused on novel formulations and solution methods for target localization using Time Differences of Arrival (TDOA) among distinct pairs of passive sensor nodes in an acoustic sensor network with known sensor positions.

Social media is an important source of real-time imagery concerning world events. One subset of social media posts which may be of particular interest are those featuring firearms. These posts can give insight into weapon movements, troop activity and civilian safety. Object detection tools offer important opportunities for insight into these images. Unfortunately, these images can be visually complex, poorly lit and generally challenging for object detection models. We present an analysis of existing gun detection datasets, and find that these datasets to not effectively address the challenge of gun detection on real-life images. Following this, we present a novel object detection pipeline. We train our pipeline on a number of datasets including one created for this investigation made up of Twitter images of the Russo-Ukrainian War. We compare the performance of our model as trained on the different datasets to baseline numbers provided by original authors as well as a YOLO v5 benchmark. We find that our model outperforms the state-of-the-art benchmarks on contextually rich, real-life-derived imagery of firearms.