Publications

Refine Results

(Filters Applied) Clear All

Multi-layered interactive energy space modeling for near-optimal electrification of terrestrial, shipboard and aircraft systems

Author:
Published in:
Annual Reviews in Control, no. 45, 2018, pp. 52-75.
R&D group:

Summary

In this paper, we introduce a basic multi-layered modeling framework for posing the problem of safe, robust and efficient design and control that may lend itself to ripping potential benefits from electrification. The proposed framework establishes dynamic relations between physical concepts such as stored energy, useful work, and wasted energy, on one hand; and modeling, simulation, and control of interactive modular complex dynamical systems, on the other. In particular, our recently introduced energy state-space modeling approach for electric energy systems is further interpreted using fundamental laws of physics in multi-physical systems, such as terrestrial energy-systems, aircrafts and ships. The interconnected systems are modeled as dynamically interacting modules. This approach is shown to be particularly well-suited for scalable optimization of large-scale complex systems. Instead of having to use simpler models, the proposed multi-layered modeling of system dynamics in energy space offers a promising basic method for modeling and controlling inter-dependencies across multi-physics subsystems for both ensuring feasible and near-optimal operation. It is illustrated how this approach can be used for understanding fundamental physical causes of inefficiencies created either at the component level or are a result of poor matching of their interactions.
READ LESS

Summary

In this paper, we introduce a basic multi-layered modeling framework for posing the problem of safe, robust and efficient design and control that may lend itself to ripping potential benefits from electrification. The proposed framework establishes dynamic relations between physical concepts such as stored energy, useful work, and wasted energy...

READ MORE

Highly Efficient All-Optical Beam Modulation Utilizing Thermo-optic Effects

Summary

Suspensions of plasmonic nanoparticles can diffract optical beams due to the combination of thermal lensing and self-phase modulation. Here, we demonstrate extremely efficient optical continuous wave (CW) beam switching across the visible range in optimized suspensions of 5-nm Au and Ag nanoparticles in non-polar solvents, such as hexane and decane. On-axis modulation of greater than 30 dB is achieved at incident beam intensities as low as 100 W/cm2 with response times under 200 μs, at initial solution transparency above 70%. No evidence of laser-induced degradation is observed for the highest intensities used. Numerical modeling of experimental data reveals thermo-optic coefficients of up to −1.3 × 10−3 /K, which, to our knowledge, is the highest observed to date in such nanoparticle suspensions.
READ LESS

Summary

Suspensions of plasmonic nanoparticles can diffract optical beams due to the combination of thermal lensing and self-phase modulation. Here, we demonstrate extremely efficient optical continuous wave (CW) beam switching across the visible range in optimized suspensions of 5-nm Au and Ag nanoparticles in non-polar solvents, such as hexane and decane...

READ MORE

Hybrid mixed-membership blockmodel for inference on realistic network interactions

Published in:
IEEE Trans. Netw. Sci. Eng., Vol. 6, No. 3, July-Sept. 2019.

Summary

This work proposes novel hybrid mixed-membership blockmodels (HMMB) that integrate three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such as mixed-membership and popularity. A rigorous inference procedure is developed for estimating the parameters of this model through iterative Bayesian updates, with targeted initialization to improve identifiability. For the estimation of mixed-membership parameters, the Cramer-Rao bound is derived by quantifying the information content in terms of the Fisher information matrix. The effectiveness of the proposed inference is demonstrated in simulations where the estimates achieve covariances close to the Cramer-Rao bound while maintaining good truth coverage. We illustrate the utility of the proposed model and inference procedure in the application of detecting a community from a few cue nodes, where success depends on accurately estimating the mixed-memberships. Performance evaluations on both simulated and real-world data show that inference with HMMB is able to recover mixed-memberships in the presence of challenging community overlap, leading to significantly improved detection performance over algorithms based on network modularity and simpler models.
READ LESS

Summary

This work proposes novel hybrid mixed-membership blockmodels (HMMB) that integrate three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such as...

READ MORE

Microsputterer with integrated ion-drag focusing for additive manufacturing of thin, narrow conductive lines

Published in:
J. Phys. D.: Appl. Phys., Vol. 51, 2018, 165603.

Summary

We report the design, modelling, and proof-of-concept demonstration of a continuously fed, atmospheric-pressure microplasma metal sputterer that is capable of printing conductive lines narrower than the width of the target without the need for post-processing or lithographic patterning. Ion drag-induced focusing is harnessed to print narrow lines; the focusing mechanism is modelled via COMSOL Multiphysics simulations and validated with experiments. A microplasma sputter head with gold target is constructed and used to deposit imprints with minimum feature sizes as narrow as 9 μm, roughness as small as 55 nm, and electrical resistivity as low as 1.1 mu Omega · m.
READ LESS

Summary

We report the design, modelling, and proof-of-concept demonstration of a continuously fed, atmospheric-pressure microplasma metal sputterer that is capable of printing conductive lines narrower than the width of the target without the need for post-processing or lithographic patterning. Ion drag-induced focusing is harnessed to print narrow lines; the focusing mechanism...

READ MORE

Next-generation embedded processors: an update

Published in:
GOMACTech Conf., 12-15 March 2018.

Summary

For mission assurance, Department of Defense (DoD) embedded systems should be designed to mitigate various aspects of cyber risks, while maintaining performance (size, weight, power, cost, and schedule). This paper reports our latest research effort in the development of a next-generation System-on-Chip (SoC) for DoD applications, which we first presented in GOMACTech 2014. This paper focuses on our ongoing work to enhance the mission assurance of its programmable processor. We will explain our updated processor architecture, justify the use of resources, and assess the processor's suitability for mission assurance.
READ LESS

Summary

For mission assurance, Department of Defense (DoD) embedded systems should be designed to mitigate various aspects of cyber risks, while maintaining performance (size, weight, power, cost, and schedule). This paper reports our latest research effort in the development of a next-generation System-on-Chip (SoC) for DoD applications, which we first presented...

READ MORE

SST asteroid search performance 2014-2017

Summary

From 2014 to 2017, the Lincoln Near-Earth Asteroid Research (LINEAR) program performed wide-area asteroid search using the 3.5-m Space Surveillance Telescope (SST) located on Atom Peak at White Sands Missile Range, N.M. The SST was developed by MIT Lincoln Laboratory (MIT/LL) for the Defense Advanced Research Projects Agency (DARPA) to advance the nation's capabilities in space situational awareness. LINEAR asteroid search using SST was funded by the National Aeronautics and Space Administration (NASA). During three years of asteroid search operations, the SST had more than 14 million observations accepted by the Minor Planet Center (MPC) and contributed to the discovery of 142 previously unknown near-Earth objects (NEOs). This paper provides a summary of SST asteroid search performance during the three years of operation at Atom Peak, and describes performance improvements achieved through processing software upgrades, refinements in search strategy, and hardware upgrades such as the successful installation of Wide-Field Camera 2 (WFC-2) in summer 2016.
READ LESS

Summary

From 2014 to 2017, the Lincoln Near-Earth Asteroid Research (LINEAR) program performed wide-area asteroid search using the 3.5-m Space Surveillance Telescope (SST) located on Atom Peak at White Sands Missile Range, N.M. The SST was developed by MIT Lincoln Laboratory (MIT/LL) for the Defense Advanced Research Projects Agency (DARPA) to...

READ MORE

Large-format Geiger-mode avalanche photodiode arrays and readout circuits

Published in:
IEEE J. Sel. Top. Quantum Electron., Vol. 24, No. 2, March/April 2018, 3800510.

Summary

Over the past 20 years, we have developed arrays of custom-fabricated silicon and InP Geiger-mode avalanche photodiode arrays, CMOS readout circuits to digitally count or time stamp single-photon detection events, and techniques to integrate these two components to make back-illuminated solid-state image sensors for lidar, optical communications, and passive imaging. Starting with 4 × 4 arrays, we have recently demonstrated 256 × 256 arrays, and are working to scale to megapixel-class imagers. In this paper, we review this progress and discuss key technical challenges to scaling to large format.
READ LESS

Summary

Over the past 20 years, we have developed arrays of custom-fabricated silicon and InP Geiger-mode avalanche photodiode arrays, CMOS readout circuits to digitally count or time stamp single-photon detection events, and techniques to integrate these two components to make back-illuminated solid-state image sensors for lidar, optical communications, and passive imaging...

READ MORE

Machine learning for medical ultrasound: status, methods, and future opportunities

Published in:
Abdom. Radiol., 2018, doi: 10.1007/s00261-018-1517-0.

Summary

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.
READ LESS

Summary

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited...

READ MORE

Trust and performance in human-AI systems for multi-domain command and control

Summary

Command and Control is one of the core tenants of joint military operations, however, the nature of modern security threats, the democratization of technology globally, and the speed and scope of information flows are stressing traditional operational paradigms, necessitating a fundamental shift to better concurrently integrate and operate across multiple physical and virtual domains. In this paper, we aim to address these challenges through the proposition of three concepts that will guide the creation of integrated human-AI Command and Control systems, inspired by recent advances and successes within the commercial sector and academia. The first concept is a framework for integration of AI capabilities into the enterprise that optimizes trust and performance within the workforce. The second is an approach for facilitating multi-domain operations though realtime creation of multi-organization multi-domain task teams by dynamic management of information abstraction, teaming, and risk control. The third is a new paradigm for multi-level data security and multi-organization data sharing that will be a key enabler of joint and coalition multi-domain operation in the future. Lastly, we propose a set of recommendations towards the research, development, and instantiation of these transformative advances in Command and Control capability.
READ LESS

Summary

Command and Control is one of the core tenants of joint military operations, however, the nature of modern security threats, the democratization of technology globally, and the speed and scope of information flows are stressing traditional operational paradigms, necessitating a fundamental shift to better concurrently integrate and operate across multiple...

READ MORE

XLab: early indications & warning from open source data with application to biological threat

Published in:
Proc. 51st Hawaii Int. Conf. on System Sciences, HICSS 2018, pp. 944-953.

Summary

XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper describes a novel system prototype that addresses threats arising from biological weapons of mass destruction. The prototype applies knowledge extraction analytics—including link estimation, entity disambiguation, and event detection—to build a knowledge base of 40 million entities and 140 million relationships from open sources. Exact and inexact subgraph matching analytics enable analysts to search the knowledge base for instances of modeled threats. The paper introduces new methods for inexact matching that accommodate threat models with temporal and geospatial patterns. System performance is demonstrated using several simplified threat models and an embedded scenario.
READ LESS

Summary

XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper...

READ MORE