Publications
UAS weather research roadmap
October 23, 2017
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-438
Topic:
R&D area:
Summary
Unmanned Aircraft System (UAS) operations in the National Airspace System (NAS) are rapidly increasing, and the trend is expected to continue as regulations are refined to allow broader access to the airspace. The unique characteristics of UAS (e.g., extensive operations in populated areas at altitudes below 500 feet, speed capability, and control systems) may drive the need for new and unique operational strategies, many of which are highly dependent on weather conditions. The objective of this study is to identify information gaps in the ability of current weather products to meet the needs of UAS operations, and provide a roadmap of research required to fill the gaps. There are several trends in the information gaps that surfaced repeatedly. A key item is the availability of weather observations, and forecasts tailored for on-airport operations are not necessarily sufficient for off-airport operations. Surveyed users indicated that airport-specific weather information (e.g., METAR, TAFs, etc.) do not readily translate to conditions at remote launch locations, which may be 10-30 miles from the nearest airport, and are influenced by local terrain, vegetation, and water sources. Moreover, the results show significantly less weather information available to support low-altitude flight than for typical manned-flight profiles. Beyond Visual Line of Sight (BVLOS) operations are found to have higher need for weather forecasts, uncertainty information, and contingency planning than Visual Line of Sight (VLOS) operations. Furthermore, the study identifies specific gaps related to how the airspace should be managed to mitigate safety and efficiency impacts to UAS operations. The research roadmap is composed of research recommendations that are derived from the aforementioned weather information gaps. In total, there are 14 specific recommendations that define the roadmap. The first two recommendations are not explicitly tied to specific gaps; rather they are based on lessons learned through the course of research in this study. The remaining recommendations are ordered such that their priority is based on their overall significance to the operation, the maturity of the operation, and any dependence among other recommendations.
Summary
Unmanned Aircraft System (UAS) operations in the National Airspace System (NAS) are rapidly increasing, and the trend is expected to continue as regulations are refined to allow broader access to the airspace. The unique characteristics of UAS (e.g., extensive operations in populated areas at altitudes below 500 feet, speed capability...
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Towards a universal CDAR device: a high performance adapter-based inline media encryptor
October 23, 2017
Conference Paper
Published in:
MILCOM 2017, 23-25 October 2017.
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Summary
As the rate at which digital data is generated continues to grow, so does the need to ensure that data can be stored securely. The use of an NSA-certified Inline Media Encryptor (IME) is often required to protect classified data, as its security properties can be fully analyzed and certified with minimal coupling to the environment in which it is embedded. However, these devices are historically purpose-built and must often be redesigned and recertified for each target system. This tedious and costly (but necessary) process limits the ability for an information system architect to leverage advances made in storage technology. Our universal Classified Data At Rest (CDAR) architecture represents a modular approach to reduce this burden and maximize interface flexibility. The core module is designed around NVMe, a high-performance storage interface built directly on PCIe. Interfacing with non-NVMe interfaces such as SATA is achieved with adapters which are outside the certification boundary and therefore can be less costly and leverage rapidly evolving commercial technology. This work includes an analysis for both the functionality and security of this architecture. A prototype was developed with peak throughput of 23.9 Gb/s at a power consumption of 8.5W, making it suitable for a wide range of storage applications.
Summary
As the rate at which digital data is generated continues to grow, so does the need to ensure that data can be stored securely. The use of an NSA-certified Inline Media Encryptor (IME) is often required to protect classified data, as its security properties can be fully analyzed and certified...
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Dynamically correlating network terrain to organizational missions
October 23, 2017
Conference Paper
Published in:
Proc. NATO IST-153/RWS-21 Workshop on Cyber Resilience, 23-25 October 2017.
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Summary
A precondition for assessing mission resilience in a cyber context is identifying which cyber assets support the mission. However, determining the asset dependencies of a mission is typically a manual process that is time consuming, labor intensive and error-prone. Automating the process of mapping between network assets and organizational missions is highly desirable but technically challenging because it is difficult to find an appropriate proxy within available cyber data for an asset's mission utilization. In this paper we discuss strategies to automate the processes of both breaking an organization into its constituent mission areas, and mapping those mission areas onto network assets, using a data-driven approach. We have implemented these strategies to mine network data at MIT Lincoln Laboratory, and provide examples. We also discuss examples of how such mission mapping tools can help an analyst to identify patterns and develop contextual insight that would otherwise have been obscure.
Summary
A precondition for assessing mission resilience in a cyber context is identifying which cyber assets support the mission. However, determining the asset dependencies of a mission is typically a manual process that is time consuming, labor intensive and error-prone. Automating the process of mapping between network assets and organizational missions...
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Lessons learned from hardware-in-the-loop testing of microgrid control systems
October 22, 2017
Conference Paper
Published in:
CIGRE 2017 Grid of the Future Symp., 22-25 Oct. 2017.
Summary
A key ingredient for the successful completion of any complex microgrid project is real-time controller hardware-in-the-loop (C-HIL) testing. C-HIL testing allows engineers to test the system and its controls before it is deployed in the field. C-HIL testing also allows for the simulation of test scenarios that are too risky or even impossible to test in the field. The results of C-HIL testing provide the necessary proof of concept and insight into any microgrid system limitations. This type of testing can also be used to create awareness among potential microgrid customers. This paper describes the modeling benefits, challenges, and lessons learned associated with C-HIL testing. The microgrid system used in this study has a 3 MW battery, 5 MW photovoltaic (PV) array, 4 MW diesel generator set (genset), and 3.5 MW combined heat and power generation system (CHP).
Summary
A key ingredient for the successful completion of any complex microgrid project is real-time controller hardware-in-the-loop (C-HIL) testing. C-HIL testing allows engineers to test the system and its controls before it is deployed in the field. C-HIL testing also allows for the simulation of test scenarios that are too risky...
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Bringing physical construction and real-world data collection into a massively open online course (MOOC)
October 18, 2017
Conference Paper
Published in:
IEEE Frontiers in Education Conf., FIE, 18-21 October 2017.
Summary
This Work-In-Progress paper details the process and lessons learned when converting a hands-on engineering minicourse to a scalable, self-paced Massively Open Online Course (MOOC). Online courseware has been part of academic and industry training and learning for decades. Learning activities in online courses strive to mimic in-person delivery by including lectures, homework assignments, software exercises and exams. While these instructional activities provide "theory and practice" for many disciplines, engineering courses often require hands-on activities with physical tools, devices and equipment. To accommodate the need for this type of learning, MIT Lincoln Laboratory's "Build A Small Radar" (BSR) course was used to explore teaching and learning strategies that support the inclusion of physical construction and real world data collection in a MOOC. These tasks are encountered across a range of engineering disciplines and the methods illustrated here are easily generalized to the learning experiences in engineering and science disciplines.
Summary
This Work-In-Progress paper details the process and lessons learned when converting a hands-on engineering minicourse to a scalable, self-paced Massively Open Online Course (MOOC). Online courseware has been part of academic and industry training and learning for decades. Learning activities in online courses strive to mimic in-person delivery by including...
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Bioelectronic measurement and feedback control of molecules in living cells
October 2, 2017
Journal Article
Published in:
Sci. Rep., Vol. 7, No. 1, 2 October 2017, 12511.
Summary
We describe an electrochemical measurement technique that enables bioelectronic measurements of reporter proteins in living cells as an alternative to traditional optical fluorescence. Using electronically programmable microfluidics, the measurement is in turn used to control the concentration of an inducer input that regulates production of the protein from a genetic promoter. The resulting bioelectronic and microfluidic negative-feedback loop then serves to regulate the concentration of the protein in the cell. We show measurements wherein a user-programmable set-point precisely alters the protein concentration in the cell with feedback-loop parameters affecting the dynamics of the closed-loop response in a predictable fashion. Our work does not require expensive optical fluorescence measurement techniques that are prone to toxicity in chronic settings, sophisticated time-lapse microscopy, or bulky/expensive chemo-stat instrumentation for dynamic measurement and control of biomolecules in cells. Therefore, it may be useful in creating a: cheap, portable, chronic, dynamic, and precise all-electronic alternative for measurement and control of molecules in living cells.
Summary
We describe an electrochemical measurement technique that enables bioelectronic measurements of reporter proteins in living cells as an alternative to traditional optical fluorescence. Using electronically programmable microfluidics, the measurement is in turn used to control the concentration of an inducer input that regulates production of the protein from a genetic...
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Command and control for multifunction phased array radar
October 1, 2017
Journal Article
Published in:
IEEE Trans. Geosci. Remote Sens., Vol. 55, No. 10, October 2017, pp. 5899-5912.
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Summary
We discuss the challenge of managing the Multifunction Phased Array Radar (MPAR) timeline to satisfy the requirements of its multiple missions, with a particular focus on weather surveillance. This command and control (C2) function partitions the available scan time among these missions, exploits opportunities to service multiple missions simultaneously, and utilizes techniques for increasing scan rate where feasible. After reviewing the candidate MPAR architectures and relevant previous research, we describe a specific C2 framework that is consistent with a demonstrated active array architecture using overlapped subarrays to realize multiple, concurrent receive beams. Analysis of recently articulated requirements for near-airport and national-scale aircraft surveillance indicates that with this architecture, 40–60% of the MPAR scan timeline would be available for the high-fidelity weather observations currently provided by the Weather Service Radar (WSR-88D) network. We show that an appropriate use of subarray generated concurrent receive beams, in concert with previously documented, complementary techniques to increase the weather scan rate, could enable MPAR to perform full weather volume scans at a rate of 1 per minute. Published observing system simulation experiments, human-in-the-loop studies and radar-data assimilation experiments indicate that high-quality weather radar observations at this rate may significantly improve the lead time and reliability of severe weather warnings relative to current observation capabilities.
Summary
We discuss the challenge of managing the Multifunction Phased Array Radar (MPAR) timeline to satisfy the requirements of its multiple missions, with a particular focus on weather surveillance. This command and control (C2) function partitions the available scan time among these missions, exploits opportunities to service multiple missions simultaneously, and...
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Super-resolution community detection for layer-aggregated multilayer networks
September 26, 2017
Journal Article
Published in:
Phys. Rev. X, Vol. 7, No. 3, July-September 2017, 031056.
Summary
Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K*. When layers are aggregated via a summation, we obtain K* is proportional to O(square root of NL/T), where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T=L decays more slowly than O(L^−1/2). Moreover, we find that thresholding the summation can, in some cases, cause K* to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.
Summary
Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on...
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Streaming graph challenge: stochastic block partition
September 12, 2017
Conference Paper
Published in:
HPEC 2017: IEEE Conf. on High Performance Extreme Computing, 12-14 September 2017.
Summary
An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity maximization. This paper describes various aspects of the challenge including: (1) the data sets and streaming graph generator, (2) the baseline partition algorithm with pseudocode, (3) an argument for the correctness of parallelizing the Bayesian inference, (4) different parallel computation strategies such as node-based parallelism and matrix-based parallelism, (5) evaluation metrics for partition correctness and computational requirements, (6) preliminary timing of a Python-based demonstration code and the open source C++ code, and (7) considerations for partitioning the graph in streaming fashion. Data sets and source code for the algorithm as well as metrics, with detailed documentation are available at GraphChallenge.org.
Summary
An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive...
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A cloud-based brain connectivity analysis tool
September 12, 2017
Conference Paper
Published in:
HPEC 2017: IEEE Conf. on High Performance Extreme Computing, 12-14 September 2017.
Summary
With advances in high throughput brain imaging at the cellular and sub-cellular level, there is growing demand for platforms that can support high performance, large-scale brain data processing and analysis. In this paper, we present a novel pipeline that combines Accumulo, D4M, geohashing, and parallel programming to manage large-scale neuron connectivity graphs in a cloud environment. Our brain connectivity graph is represented using vertices (fiber start/end nodes), edges (fiber tracks), and the 3D coordinates of the fiber tracks. For optimal performance, we take the hybrid approach of storing vertices and edges in Accumulo and saving the fiber track 3D coordinates in flat files. Accumulo database operations offer low latency on sparse queries while flat files offer high throughput for storing, querying, and analyzing bulk data. We evaluated our pipeline by using 250 gigabytes of mouse neuron connectivity data. Benchmarking experiments on retrieving vertices and edges from Accumulo demonstrate that we can achieve 1-2 orders of magnitude speedup in retrieval time when compared to the same operation from traditional flat files. The implementation of graph analytics such as Breadth First Search using Accumulo and D4M offers consistent good performance regardless of data size and density, thus is scalable to very large dataset. Indexing of neuron subvolumes is simple and logical with geohashing-based binary tree encoding. This hybrid data management backend is used to drive an interactive web-based 3D graphical user interface, where users can examine the 3D connectivity map in a Google Map-like viewer. Our pipeline is scalable and extensible to other data modalities.
Summary
With advances in high throughput brain imaging at the cellular and sub-cellular level, there is growing demand for platforms that can support high performance, large-scale brain data processing and analysis. In this paper, we present a novel pipeline that combines Accumulo, D4M, geohashing, and parallel programming to manage large-scale neuron...
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