Abstract This paper presents the development and evaluation of a deep neural network model for the detection of naval surface vessel using laboratory-generated datasets. By employing chroma-key technology, images of a scale model naval vessel were superimposed onto realistic maritime backgrounds to create a diverse training dataset. Fine-tuned with these datasets and evaluated using the YOLOv8 framework, the model achieved high precision and recall in identifying the naval surface vessel despite data limitations. This zero-shot learning approach, validated through extensive testing, supports visual navigation and target identification in GPS/RF-denied environments, advancing autonomous maritime operations and aligning with the United States Navy strategy to leverage AI/ML for military enhancement.

The Sun Network Software Environment (NSE) is a network-based object manager for software development.The NSE supports parallel development through an optimistic concurrency control mechanism, in which developers do not acquire locks before modifying objects.Instead, developers copy objects, modify the copies, and merge the modified objects with the originals.Objects managed by the NSE are typed, and the set of types can be extended by tool builders.The NSE is designed to work with heterogeneous implementations and poor communication.

Abstract Dbxtool is a window‐ and mouse‐based debugger for C, Pascal and FORTRAN programs running on Sun workstations. Its use of the mouse as the primary input mechanism eliminates the need to type variables, line numbers, breakpoints and most commands. Its multiple windows provide several qualitatively different perspectives on the debugging problem. Compared to the Unix 4.2 BSD dbx from which it is derived, it has been extended with the abilities to debug multiple‐process programs, already‐running processes, and the Sun Operating System kernel.