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Terminal Doppler Weather Radar enhancements

Author:
Published in:
IEEE Radar Conf., 10 May 2010, pp. 1245-1249.

Summary

The design of an open radar data acquisition system for the Terminal Doppler Weather Radar is presented. Adaptive signal transmission and processing techniques that take advantage of the enhanced capabilities of this new system are also discussed. Results displaying data quality improvements with respect to problems such as range-velocity ambiguity and moving clutter are shown.
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Summary

The design of an open radar data acquisition system for the Terminal Doppler Weather Radar is presented. Adaptive signal transmission and processing techniques that take advantage of the enhanced capabilities of this new system are also discussed. Results displaying data quality improvements with respect to problems such as range-velocity ambiguity...

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Improvement of SOI MOSFET RF performance by implant optimization

Published in:
IEEE Microw. Wirel. Compon. Lett., Vol. 20, No. 5, May 2010, pp. 271-273.

Summary

The characteristics of silicon on insulator MOSFETs are modified to enhance the RF performance by varying channel implants. Without adding new masks or fabrication steps to the standard CMOS process, this approach can be easily applied in standard foundry fabrication. The transconductance, output resistance, and breakdown voltage can be increased by eliminating channel and drain extension implants. As a result, the fmax of the modified n-MOSFET with a 150 nm gate length exceeds 120 GHz, showing a 20% improvement over the standard MOSFET for digital circuits on the same wafer.
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Summary

The characteristics of silicon on insulator MOSFETs are modified to enhance the RF performance by varying channel implants. Without adding new masks or fabrication steps to the standard CMOS process, this approach can be easily applied in standard foundry fabrication. The transconductance, output resistance, and breakdown voltage can be increased...

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Signal processing algorithms for the Terminal Doppler Weather Radar: Build 2

Author:
Published in:
MIT Lincoln Laboratory Report ATC-363

Summary

As a new radar data acquisition system (RDA) was developed for the Terminal Doppler Weather Radar (TDWR), enhanced signal processing algorithms taking advantage of its increased capabilities were also developed. The primary goals of protecting the base data estimates from range-aliased signals and providing reliable velocity dealiasing were achieved through multiple pulse repetition interval (PRI) and phase coding methods. An innovative radial-by-radial adaptive selection process was used to take full advantage of the different techniques, the first time such an approach has been implemented for weather radars. Improvement in clutter filtering was also achieved. This report discusses in detail these new RDA signal processing algorithms.
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Summary

As a new radar data acquisition system (RDA) was developed for the Terminal Doppler Weather Radar (TDWR), enhanced signal processing algorithms taking advantage of its increased capabilities were also developed. The primary goals of protecting the base data estimates from range-aliased signals and providing reliable velocity dealiasing were achieved through...

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CIWS product description, revision 1.0

Author:
Published in:
MIT Lincoln Laboratory Report ATC-355

Summary

Lincoln Laboratory has developed a set of information models for the encoding and distribution of data products from the National Corridor Integrated Weather System (CIWS) prototype, currently operating at Lincoln Laboratory in Lexington, Massachusetts. CIWS data products can be categorized as gridded and non-gridded. Gridded products are typically expressed as rectangular arrays whose elements contain a data value coinciding with uniformly-spaced observations or computed results on a 2-D surface. Gridded data arrays map to earth's surface through a map projection, for example, Lambert Conformal or Lambert Azimuthal Equal-Area. Non-gridded data products express observations or computed results associated with singular or sparsely distributed sets of geo-spatial locations such as points, curves, or contours. CIWS prototype data products were used to develop, refine, and evaluate reference information models for the CIWS gridded and non-gridded data. Data packaging methods were evaluated and selected on the basis of public-domain open-source availability and metadata support. Network Common Data Format (NetCDF), provided by Unidata, was selected as the information model for gridded CIWS products. For the non-gridded products, XML schemas have been developed along with sample XML instances to illustrate schema-compliant product encodings. These models follow and extend upon a number of Open Geospatial Consortium (OGC) and ISO standards including Geography Markup Language (GML), Observations and Measurements (OM), and Eurocontrol's Weather Exchange Model (WXXM). This document is intended to serve as a reference for the description of CIWS data product files.
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Summary

Lincoln Laboratory has developed a set of information models for the encoding and distribution of data products from the National Corridor Integrated Weather System (CIWS) prototype, currently operating at Lincoln Laboratory in Lexington, Massachusetts. CIWS data products can be categorized as gridded and non-gridded. Gridded products are typically expressed as...

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The application of statistical relational learning to a database of criminal and terrorist activity

Published in:
SIAM Conf. on Data Mining, 29 April - 1 May 2010.

Summary

We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of terrorist groups or as input to inform human decision makers.
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Summary

We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data...

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Data diodes in support of trustworthy cyber infrastructure

Published in:
6th Annual Cyber Security and Information Intelligence Research Workshop, Cyber Security and Information Intelligence Challenges and Strategies, CSIIRW10, 21 April 2010.

Summary

Interconnections between process control networks and enterprise networks has resulted in the proliferation of standard communication protocols in industrial control systems which exposes instrumentation, control systems, and the critical infrastructure components they operate to a variety of cyber attacks. Various standards and technologies have been proposed to protect industrial control systems against cyber attacks and to provide them with confidentiality, integrity, and availability. Among these technologies, data diodes provide protection of critical systems by the means of physically enforcing traffic direction on the network. In order to deploy data diodes effectively, it is imperative to understand the protection they provide, the protection they do not provide, their limitations, and their place in the larger security infrastructure. In this work, we briefly review the security challenges in an industrial control system, study data diodes, their functionalities and limitations, and propose a scheme for their effective deployment in trusted process control networks (TPCNs.)
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Summary

Interconnections between process control networks and enterprise networks has resulted in the proliferation of standard communication protocols in industrial control systems which exposes instrumentation, control systems, and the critical infrastructure components they operate to a variety of cyber attacks. Various standards and technologies have been proposed to protect industrial control...

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Improved Monte Carlo sampling for conflict probability estimation

Published in:
51st AIAA/ASME/AHS/ACS Structures, Structural Dynamics, and Materials Conf., 12-15 April 2010.

Summary

Probabilistic alerting systems for airborne collision avoidance often depend upon accurate estimates of the probability of conflict. Analytical, numerical approximation, and Monte Carlo methods have been applied to conflict probability estimation. The advantage of a Monte Carlo approach is the greater flexibility afforded in modeling the stochastic behavior of aircraft encounters, but typically many samples are required to provide an adequate conflict probability estimate. One approach to improve accuracy with fewer samples is to use importance sampling, where trajectories are sampled according to a proposal distribution that is different from the one specified by the model. This paper suggests several different sample proposal distributions and demonstrates how they result in significantly improved estimates.
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Summary

Probabilistic alerting systems for airborne collision avoidance often depend upon accurate estimates of the probability of conflict. Analytical, numerical approximation, and Monte Carlo methods have been applied to conflict probability estimation. The advantage of a Monte Carlo approach is the greater flexibility afforded in modeling the stochastic behavior of aircraft...

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Hybridization process for back-illuminated silicon Geiger-mode avalanche photodiode arrays

Published in:
SPIE Vol. 7681, Advanced Photon Counting Techniques IV, 5 April 2010, 76810P.

Summary

We present a unique hybridization process that permits high-performance back-illuminated silicon Geiger-mode avalanche photodiodes (GM-APDs) to be bonded to custom CMOS readout integrated circuits (ROICs) - a hybridization approach that enables independent optimization of the GM-APD arrays and the ROICs. The process includes oxide bonding of silicon GM-APD arrays to a transparent support substrate followed by indium bump bonding of this layer to a signal-processing ROIC. This hybrid detector approach can be used to fabricate imagers with high-fill-factor pixels and enhanced quantum efficiency in the near infrared as well as large-pixel-count, small-pixel-pitch arrays with pixel-level signal processing. In addition, the oxide bonding is compatible with high-temperature processing steps that can be used to lower dark current and improve optical response in the ultraviolet.
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Summary

We present a unique hybridization process that permits high-performance back-illuminated silicon Geiger-mode avalanche photodiodes (GM-APDs) to be bonded to custom CMOS readout integrated circuits (ROICs) - a hybridization approach that enables independent optimization of the GM-APD arrays and the ROICs. The process includes oxide bonding of silicon GM-APD arrays to...

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Noncontact detection of homemade explosive constituents via photodissociation followed by laser-induced fluorescence

Published in:
Opt. Express, Vol. 18, No. 6, 15 March 2010, pp. 5399-5406.

Summary

Noncontact detection of the homemade explosive constituents urea nitrate, nitromethane and ammonium nitrate is achieved using photodissociation followed by laser-induced fluorescence (PD-LIF). Our technique utilizes a single ultraviolet laser pulse (~7 ns) to vaporize and photodissociate the condensed-phase materials, and then to detect the resulting vibrationally-excited NO fragments via laser-induced fluorescence. PD-LIF excitation and emission spectra indicate the creation of NO in vibrationally-excited states with significant rotational energy, useful for low-background detection of the parent compound. The results for homemade explosives are compared to one another and 2,6- dinitrotoluene, a component present in many military explosives.
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Summary

Noncontact detection of the homemade explosive constituents urea nitrate, nitromethane and ammonium nitrate is achieved using photodissociation followed by laser-induced fluorescence (PD-LIF). Our technique utilizes a single ultraviolet laser pulse (~7 ns) to vaporize and photodissociate the condensed-phase materials, and then to detect the resulting vibrationally-excited NO fragments via laser-induced...

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Detection and simulation of scenarios with hidden Markov models and event dependency graphs

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 15 March 2010, pp. 5434-5437.

Summary

The wide availability of signal processing and language tools to extract structured data from raw content has created a new opportunity for the processing of structured signals. In this work, we explore models for the simulation and recognition of scenarios - i.e., time sequences of structured data. For simulation, we construct two models - hidden Markov models (HMMs) and event dependency graphs. Combined, these two simulation methods allow the specification of dependencies in event ordering, simultaneous execution of multiple scenarios, and evolving networks of data. For scenario recognition, we consider the application of multi-grained HMMs. We explore, in detail, mismatch between training scenarios and simulated test scenarios. The methods are applied to terrorist scenario detection with a simulation coded by a subject matter expert.
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Summary

The wide availability of signal processing and language tools to extract structured data from raw content has created a new opportunity for the processing of structured signals. In this work, we explore models for the simulation and recognition of scenarios - i.e., time sequences of structured data. For simulation, we...

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