Stevens Institute of Technology is a private research university in New Jersey specializing in engineering, business, and systems innovation. It emphasizes applied learning and technological entrepreneurship. NEC Labs America partners with Stevens Institute of Technology on acoustic sensing, time-series modeling, and resilient speech analytics. Please read about our latest news and collaborative publications with Stevens Institute of Technology.
Learn about the amazing group of interns who joined us at Princeton and San Jose campuses this summer. Their hard work, fresh perspectives, and dedication have truly made an impact across the board, from cutting-edge research projects to innovative software development initiatives.
Subsea cables are critical components of offshore wind turbines and are subjected to scour. Monitoring the scour conditions of subsea cables plays significant roles in improving safety and operation efficiency and reducing the levelized cost of electricity. This paper presents a feasibility study on monitoring subsea cables using distributed fiber optic sensors (DFOS), aiming to evaluate the technical and economic performance of utilizing DFOS to detect, locate, and quantify scour conditions. Laboratory experiments were conducted to test the response ofDFOS measurements to the change of support conditions which were used to simulate scour effects, and a finite element model was developed to investigate the impact of scour on the mechanical responses of subsea cables in different scour scenarios. Economic analysis of three methods, involving the use of DFOS, discrete sensors, and underwater robots, is performed via a case study. The results showed that the proposed method has technical and economic benefits for monitoring subsea cables. This research offers insights into monitoring subsea structuresfor offshore wind turbines.
A 100-meter-long fiber optic cable was installed at the bottom of a water tank at the Davidson Laboratory, together with a hydrophone for reference. The water tank is approximately 2.5 meters deep and 95 meters long; the tank also employs a 6-paddle wavemaker which can generate programmable surface waves. A 155-cm-long model boat weighing 6.5 kilograms was automatically dragged on the surface of the tank via an electrical towing mechanism. The movement of the model boat along the fiber cable and over the hydrophone was recorded using a commercially available NEC Distributed Acoustic Sensing (DAS) system and simultaneously by a hydrophone. The experiments were repeated with and without the artificially generated surface waves. The data obtained from the hydrophone and the DAS system are presented and compared. The results show the compatibility between the DAS data and the hydrophone data. More importantly, ourresults show that it is possible to measure the surface waves and to detect a surface vessel approaching the sensor by only using the time domain analysis in terms of detected total energy over time.
Learn about the amazing group of interns who joined us at Princeton and San Jose campuses this summer. Their hard work, fresh perspectives, and dedication have truly made an impact across the board, from cutting-edge research projects to innovative software development initiatives.
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature. Estimating 3D coordinates for each pixel on the object surface holds great potential as it provides dense 2D-3D geometric constraints for the underlying PnP problem. However, high-quality ground truth supervision is not available in driving scenes due to sparsity and various artifacts of Lidar data, as well as the practical infeasibility of collecting per-instance CAD models. In this work, we present NeurOCS, a framework that uses instance masks and 3D boxes as input to learn 3D object shapes by means of differentiable rendering, which further serves as supervision for learning dense object coordinates. Our approach rests on insights in learning a category-level shape prior directly from real driving scenes, while properly handling single-view ambiguities. Furthermore, we study and make critical design choices to learn object coordinates more effectively from an object-centric view. Altogether, our framework leads to new state-of-the-art in monocular 3D localization that ranks 1st on the KITTI-Object benchmark among published monocular methods.