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This issue of the Johns Hopkins APL Technical Digest focuses on the Johns Hopkins University Applied Physics Laboratory (APL) Discovery Program, a 2-year rotational opportunity for new college graduates that consists of four rotation assignments spanning multiple technical areas across the Laboratory. In addition to featuring reflections from program alumni and host supervisors and an overview of the program’s training component, the issue highlights the technical contributions of some of the staff members who have been part of the program or are currently part of it. The articles in this issue showcase the core competencies of APL but also truly highlight the core tenets of the Discovery Program: broad exposure, career foundations, and professional connections. The articles amplify how the Discovery Program accomplishes its vision of a persistent, collaborative, and innovative network of impactful staff who will lead us into the future. Some of the staff members in the Discovery Program will enable APL’s future defining innovations.

The Johns Hopkins University Applied Physics Laboratory (APL) Link-Layer Identification of Device Signatures (LLIDS) research effort uses machine learning techniques to identify unique wireless device signatures from patterns in link-layer data. Identifying signatures can increase situational awareness, assist in estimating crowd sizes, provide pattern of life, and protect facilities and infrastructure through activity surveillance. Link-layer Wi-Fi data are unique because they can be collected without access to a network and with devices that have low size, weight, and power (SWaP) requirements. The LLIDS multilayer system design breaks down link-layer data into unique device signatures using a combination of pattern recognition and state-of-the-art algorithms.

The Johns Hopkins University Applied Physics Laboratory (APL) is actively developing new capabilities for genomic surveillance of viruses. APL genomicists analyze, process, and visualize viral genomic data for several sponsor organizations that require those data to inform clinical, research, and public policy decisions. Many of the final products from these processes are delivered to sponsors as static reports or slide presentations, but it can be arduous to review or extract pertinent information from these documents. APL genomicists wanted to improve their sponsors’ ability to analyze their data and rapidly identify genomic samples or sequences they find important for decision-making. With this goal in mind, a group of APL software engineers developed the Automated Genomics Application for Variant Exploration (AGAVE). AGAVE is an interactive, intuitive web-based tool where researchers can explore and analyze genomic data, draw new connections between data points, and understand the significance behind genomic variants quickly. Researchers can view their sequence data, choose a reference genome with which to compare the data, visualize the 3-D structure of proteins that would be created from particular segments of DNA, and export those visualizations as easily shared image files. AGAVE is still under development and currently supports only influenza genomes, but as it matures and its user base grows, it will expand beyond influenza to include other viruses such as SARS-CoV-2 and even bacterial genomes.

In the last two decades, considerable increases in computing power and available data have led to an analytics and machine learning (ML) revolution. To make knowledge management less cumbersome for human operators, a team of researchers at the Johns Hopkins University Applied Physics Laboratory (APL) proposes an ML–based method to help automate knowledge management. This method discovers new data, represents it with descriptive metadata, automatically categorizes the metadata, auto-populates a data catalog with data sets, and evaluates the new data sets for data fusion options. We focus on a framework that can potentially leverage human– machine teaming to significantly reduce the human resource burden to develop and maintain an accurate accounting of existing data and capabilities within an organization. We explored numerous ML options to test our core hypothesis—that ML techniques can be employed to reliably determine the fundamental topic that an unknown data set represents, leading to increasingly granular data set recognition as more characterization and context information can be mined in the metadata extraction phase. Ultimately, we demonstrated that multiple classifier techniques exist that can predict data set topics with close to 90% accuracy, and some with 60%– 80% accuracy, across multiple topics.

Acoustic emissions analysis can provide key information for monitoring the structural integrity of a system, such as the behavior of bone under various loading conditions and other complex biomechanical applications. However, when analyzing acoustic emissions data from complex systems, including systems that experience high-rate (10³ s^–1) loading, complex bending modes, unique shape effects, and multiple failure mechanisms, it is difficult to extract meaningful information and relationships because of an abundance of confounding factors. This article presents a methodology developed at the Johns Hopkins Applied Physics Laboratory (APL) for understanding fracture and characterizing acoustic signatures with distinct failure modes, leveraging techniques such as independent component analysis, self-organizing maps, and K-means clustering algorithms.

Computed tomography (CT) scans, because of their ability to differentiate tissue densities, have been widely used to evaluate lung health. Recent studies such as COPDGene have collected inhalation and exhalation CT scans from thousands of subjects, promising insight into the mechanical properties of lung tissue. These paired scans must often be spatially aligned (i.e., registered) to extract biomarkers describing the movement of lung tissue that may correlate with disease. Unfortunately, the relationship between registration and biomarker error is poorly characterized, a challenge that must be addressed before registration-based biomarkers can be used in clinical practice. In our analysis, we consider three registration-based biomarkers (Jacobian determinant, anisotropic deformation index, and slab-rod index) and demonstrate their sensitivity to modeled registration error. We provide a range of errors for a given biomarker, highlighting how both the magnitude of registration error and correlations between vectors in the registration error field can influence biomarker error. We then describe a method to measure the error field for a particular registration algorithm and compare it with modeled registration error. These estimates enable selection of an appropriate registration error model, which improves understanding of biomarker uncertainty. Quantifying the relationship between registration and biomarker error is crucial because it may inform the selection of a registration algorithm to reduce error in new research studies, and in turn, result in robust imaging biomarkers for disease characterization.

Modeling and simulation (M&S) is a critical step throughout the systems engineering process for developing and fielding a combat system. Verification and, more specifically, validation are essential to determining whether a simulation is credible and reliable. Although policy and guidance increasingly emphasizes the importance of rigorous validation founded in the application of strong statistical analysis, implementation continues to be challenging. As a result, test organizations and statisticians have been interested in developing a robust approach for measuring the performance of the validation methods used to assess model accuracy. The Johns Hopkins University Applied Physics Laboratory (APL) developed a flexible and extensible framework to evaluate the performance of the validation methods. The framework provides the modularity to evaluate multiple validation methods and is sufficiently generic to support assessment of multiple simulation models. This article details the framework design and the analysis of multiple statistical validation methods, including an exemplar assessment of the methods applied for a recently accredited missile system simulation.

The Johns Hopkins University Applied Physics Laboratory (APL) is additively manufacturing space instruments to meet specific science objectives. One example is an electron collimator, built using additive manufacturing technology, that will fly on the European Space Agency’s JUpiter ICy moons Explorer (JUICE) mission set to launch in 2022. The collimator is the first-ever additively manufactured mechanical component to be both fabricated and qualified for spaceflight at APL. By using metal additive techniques, the APL team achieved complex geometries that could not have been obtained with conventional manufacturing. The intricate collimators, each about the size of a quarter and peppered with hundreds of tiny holes, are assembled in a spherically focused arrangement. They confine particle trajectories within the face of the detectors in the instrument. Extensive collaboration between APL’s Research and Exploratory Development Department and Space Exploration Sector led to the successful development and qualification of the flight collimator in just 2 years. The innovative capabilities of additive manufacturing will become an integral part of future space missions.

With a rich history dating back over a century, electronic warfare has become a powerful tool at the warfighter’s disposal. However, as technology continues to advance, so must the capabilities of electronic warfare systems and the tools used to evaluate them. A team at the Johns Hopkins University Applied Physics Laboratory (APL) leveraged a unique combination of technical and operational expertise to develop a streamlined analysis approach to minimize turnaround time for analysis. This article overviews this approach and highlights a robust digital signal processing tool providing quick and thorough analysis results for mission-critical and operationally relevant test data.

Merchant vessels travel across the ocean daily to deliver goods and transport cargo or passengers. Understanding the forecasted locations of these vessels is important for many reasons, including collision avoidance. Currently, their captains rely on radar, a global positioning system (GPS) satellite fix, and the Automatic Identification System (AIS) to maintain timely awareness of their surroundings. This article describes a Johns Hopkins University Applied Physics Laboratory (APL) team’s research into using a Kalman filter to improve forecasts of vessels’ locations. When provided historical geospatial data that contain uncertainties, the Kalman filter algorithm provides a means to estimate future locations of moving objects. The APL team confirmed that when using GPS and AIS data, the Kalman filter forecasting tool can predict the future location of a vessel 90% of the time within 15 nautical miles for 12 h into the future.

The Johns Hopkins University Applied Physics Laboratory (APL) is leading Dragonfly, a mission to study the prebiotic chemistry of Titan, one of Saturn’s moons. Given Titan’s diverse surface environments, mobility is crucial to the science mission, so controls engineers are faced with the challenge of designing an autonomous flight-control system for an aerial vehicle that will operate in uncertain environments. Part of the flight controller development approach involves testing with a half-scale test vehicle in an Earth environment; and one part of this process is system identification. Here, we detail the design and testing of the first round of system identification experiments with the test vehicle in which random-phase multisines were injected into the attitude commands during hover. Four experiments were performed using the half-scale test vehicle. Because of significant wind disturbances, the collected data had low coherence and were ultimately unsuitable for nonparametric frequency response estimation. System identification is an iterative process, and we present several planned ways to improve the coherence of the flight data.

Satellites are excellent platforms for communications, sensing, imaging, and navigation, providing the “high ground” for large-area fields of view and low-loss free-space paths for inter-satellite links. As development and production costs decline, large constellations (upward of thousands of satellites) are planned for near-future launches by both private and public entities. Designing these constellations is challenging because of the large trade space that includes altitude, inclination, total number of satellites, distribution of satellites in planes, and phasing between satellite planes. In this article, we discuss a new constellation design (patent pending), which we call a Waves constellation, developed by the Johns Hopkins University Applied Physics Laboratory (APL) to provide optimal coverage in a given latitude band. Further, we discuss our work to speed up the analysis of satellite constellation coverage, which can be used with the Waves geometry or any arbitrary constellation geometry.

This article outlines recent Johns Hopkins University Applied Physics Laboratory (APL) work on a fiber optic recirculating loop (RCL) system and describes some of the important design decisions. Optical RCLs were originally designed as a means to study long-haul data transmission systems in a compact, less-expensive manner. For this project, however, the RCL is used to transmit repeated radio frequency (RF) signals within a larger optical system. At its core, an RCL consists of a length of fiber and an amplifier. An optical switch is used to let an encoded RF signal enter the loop, while an optical coupler is used to let the encoded RF signal exit the loop. We made multiple design decisions while making this system. Most important, chromatic dispersion of the optical fiber disrupts transmission of the recirculated RF signal. To account for this, we evaluated multiple optical fiber types, encoded the RF signal using a single-sideband technique, and incorporated a programmable optical filter with dispersion compensation capabilities. Moreover, polarization-dependent loss (PDL) and polarization-mode dispersion (PMD) within optical components are compounded as light recirculates. To accommodate for this, we incorporated a polarization scrambler in the design. In this article, we walk through the RCL design process and mention the contributions of each optical component to the final design.

In this article, four recent APL Discovery Program alumni reflect on their experiences in the program.

In this article, four supervisors reflect on how APL Discovery Program staff members have made a positive impact on their teams.

Building career foundations is one of the three core tenets of the Discovery Program at Johns Hopkins University Applied Physics Laboratory (APL). New college graduates selected for this cohort-based rotational program create these foundations through the program’s carefully constructed training and mentoring component, which was developed collaboratively by Discovery Program leadership and APL’s Talent Development Office. To ensure that training is immediately relevant and useful, training opportunities are sequenced strategically throughout the program and offered at just the right time in the staff members’ evolution. This article describes the approach to helping APL’s Discovery Program staff members build strong foundations that will serve them well throughout their careers.

This infographic offers highlights from APL’s Discovery Program.