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Correlated encounter model for cooperative aircraft in the National Airspace System, version 1.0

Published in:
MIT Lincoln Laboratory Report ATC-344

Summary

This document describes a new cooperative aircraft encounter model for the National Airspace System (NAS). The model is used to generate random close encounters between transponder-equipped (cooperative) aircraft in fast-time Monte Carlo simulations to evaluate collision avoidance system concepts. An extensive set of radar data from across the United States, including more than 120 sensors and collected over a period of nine months, was used to build the statistical relationships in the model to ensure that the encounters that are generated are representative of actual events in the airspace.
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Summary

This document describes a new cooperative aircraft encounter model for the National Airspace System (NAS). The model is used to generate random close encounters between transponder-equipped (cooperative) aircraft in fast-time Monte Carlo simulations to evaluate collision avoidance system concepts. An extensive set of radar data from across the United States...

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Characterization of a three-dimensional SOI integrated-circuit technology

Published in:
2008 IEEE Int. SOI Conf. Proc., 6 October 2008, pp. 109-110.

Summary

At Lincoln Laboratory, we have established a three dimensional (3D) integrated circuit (IC) technology that has been developed and demonstrated over eight designs, bonding two or three active circuit layers or tiers to form monolithically integrated 3D circuits. This technology has been used to successfully demonstrate a large-area 8 x 8 mm2 high-3D-via-count 1024 x 1024 visible image, a 64 x 64 laser radar focal plane based on single-photon-sensitive avalanche photodiodes, and a 10Gb/s/pin low power interconnect for 3DICs. 3DIC technology in our most recently completed 3D multiproject (3DM2) run includes three active fully-depleted-SOI (FDSOI) circuit tiers, eleven interconnect-metal layers, and dense unrestricted 3D vias interconnecting stacked circuit layers, as shown in Figure 1. While we continue our efforts to scale our 3DIC technology and increase 3D via density, we are also working to improve our understanding of 3D integration impact on transistor and process monitor circuits. In this paper, we describe our process and test results after single tier circuit fabrication as well as after three-tier integration, determine impact of 3D vias on ring oscillator performance, and demonstrate functionality of single and multi-tier circuits of varying complexity.
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Summary

At Lincoln Laboratory, we have established a three dimensional (3D) integrated circuit (IC) technology that has been developed and demonstrated over eight designs, bonding two or three active circuit layers or tiers to form monolithically integrated 3D circuits. This technology has been used to successfully demonstrate a large-area 8 x...

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Operational usage of the Route Availability Planning Tool during the 2007 convective weather season : executive summary

Published in:
MIT Lincoln Laboratory Report ATC-339-1

Summary

The Route Availability Planning Tool (RAPT) is an integrated weather/air traffic management decision support tool that has been designed to help traffic managers better anticipate weather impacts on jet routes and thus improve NY departure route usage efficiency. A field study was conducted in 2007 to evaluate RAPT technical performance at forecasting route blockage, to assess RAPT operational use during adverse weather, and to evaluate RAPT benefits. The operational test found that RAPT guidance was operationally sound and timely in many circumstances. RAPT applications included increased departure route throughput, more efficient reroute planning, and more timely decision coordination. Estimated annual NY departure delay savings attributed to RAPT in 2007 totaled 2,300 hours, with a cost savings of $7.5 M. The RAPT field study also sought to develop a better understanding of NY traffic flow decision-making during convective weather impacts since the RAPT benefi ts in 2007 were significantly limited by a number of factors other than direct weather impacts. Observations were made of the multi-facility departure management decision chain, the traffic management concerns and responsibilities at specific FAA facilities, and the procedures and pitfalls of the current process for capturing and disseminating key information such as route/fix availability and restrictions.
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Summary

The Route Availability Planning Tool (RAPT) is an integrated weather/air traffic management decision support tool that has been designed to help traffic managers better anticipate weather impacts on jet routes and thus improve NY departure route usage efficiency. A field study was conducted in 2007 to evaluate RAPT technical performance...

READ MORE

Operational usage of the Route Availability Planning Tool during the 2007 convective weather season

Published in:
MIT Lincoln Laboratory Report ATC-339

Summary

The Route Availability Planning Tool (RAPT) is an integrated weather/air traffic management decision support tool that has been designed to help traffic managers better anticipate weather impacts on jet routes and thus improve NY departure route usage efficiency. A field study was conducted in 2007 to evaluate RAPT technical performance at forecasting route blockage, to assess RAPT operational use during adverse weather, and to evaluate RAPT benefits. The operational test found that RAPT guidance was operationally sound and timely in many circumstances. RAPT applications included increased departure route throughput, more efficient reroute planning, and more timely decision coordination. Estimated annual NY departure delay savings attributed to RAPT in 2007 totaled 2,300 hours, with a cost savings of $7.5 M. The RAPT field study also sought to develop a better understanding of NY traffic flow decision-making during convective weather impacts since the RAPT benefits in 2007 were significantly limited by a number of factors other than direct weather impacts. Observations were made of the multi-facility departure management decision chain, the traffic management concerns and responsibilities at specific FAA facilities, and the procedures and pitfalls of the current process for capturing and disseminating key information such as route/fix availability and restrictions.
READ LESS

Summary

The Route Availability Planning Tool (RAPT) is an integrated weather/air traffic management decision support tool that has been designed to help traffic managers better anticipate weather impacts on jet routes and thus improve NY departure route usage efficiency. A field study was conducted in 2007 to evaluate RAPT technical performance...

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InP-based single-photon detector arrays with asynchronous readout integrated circuits

Summary

We have developed and demonstrated a highduty- cycle asynchronous InGaAsP-based photon counting detector system with near-ideal Poisson response, roomtemperature operation, and nanosecond timing resolution for near-infrared applications. The detector is based on an array of Geiger-mode avalanche photodiodes coupled to a custom integrated circuit that provides for lossless readout via an asynchronous, nongated architecture. We present results showing Poisson response for incident photon flux rates up to 10 million photons per second and multiple photons per 3-ns timing bin.
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Summary

We have developed and demonstrated a highduty- cycle asynchronous InGaAsP-based photon counting detector system with near-ideal Poisson response, roomtemperature operation, and nanosecond timing resolution for near-infrared applications. The detector is based on an array of Geiger-mode avalanche photodiodes coupled to a custom integrated circuit that provides for lossless readout via...

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Language, dialect, and speaker recognition using Gaussian mixture models on the cell processor

Published in:
Twelfth Annual High Performance Embedded Computing Workshop, HPEC 2008, 23-25 September 2008.

Summary

Automatic recognition systems are commonly used in speech processing to classify observed utterances by the speaker's identity, dialect, and language. These problems often require high processing throughput, especially in applications involving multiple concurrent incoming speech streams, such as in datacenter-level processing. Recent advances in processor technology allow multiple processors to reside within the same chip, allowing high performance per watt. Currently the Cell Broadband Engine has the leading performance-per-watt specifications in its class. Each Cell processor consists of a PowerPC Processing Element (PPE) working together with eight Synergistic Processing Elements (SPE). The SPEs have 256KB of memory (local store), which is used for storing both program and data. This paper addresses the implementation of language, dialect, and speaker recognition on the Cell architecture. Classically, the problem of performing speech-domain recognition has been approached as embarrassingly parallel, with each utterance being processed in parallel to the others. As we will discuss, efficient processing on the Cell requires a different approach, whereby computation and data for each utterance are subdivided to be handled by separate processors. We present a computational model for automatic recognition on the Cell processor that takes advantage of its architecture, while mitigating its limitations. Using the proposed design, we predict a system able to concurrently score over 220 real-time speech streams on a single Cell.
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Summary

Automatic recognition systems are commonly used in speech processing to classify observed utterances by the speaker's identity, dialect, and language. These problems often require high processing throughput, especially in applications involving multiple concurrent incoming speech streams, such as in datacenter-level processing. Recent advances in processor technology allow multiple processors to...

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A comparison of subspace feature-domain methods for language recognition

Summary

Compensation of cepstral features for mismatch due to dissimilar train and test conditions has been critical for good performance in many speech applications. Mismatch is typically due to variability from changes in speaker, channel, gender, and environment. Common methods for compensation include RASTA, mean and variance normalization, VTLN, and feature warping. Recently, a new class of subspace methods for model compensation have become popular in language and speaker recognition--nuisance attribute projection (NAP) and factor analysis. A feature space version of latent factor analysis has been proposed. In this work, a feature space version of NAP is presented. This new approach, fNAP, is contrasted with feature domain latent factor analysis (fLFA). Both of these methods are applied to a NIST language recognition task. Results show the viability of the new fNAP method. Also, results indicate when the different methods perform best.
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Summary

Compensation of cepstral features for mismatch due to dissimilar train and test conditions has been critical for good performance in many speech applications. Mismatch is typically due to variability from changes in speaker, channel, gender, and environment. Common methods for compensation include RASTA, mean and variance normalization, VTLN, and feature...

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A hybrid SVM/MCE training approach for vector space topic identification of spoken audio recordings

Published in:
INTERSPEECH 2008, 22-26 September 2008, pp. 2542-2545.

Summary

The success of support vector machines (SVMs) for classification problems is often dependent on an appropriate normalization of the input feature space. This is particularly true in topic identification, where the relative contribution of the common but uninformative function words can overpower the contribution of the rare but informative content words in the SVM kernel function score if the feature space is not normalized properly. In this paper we apply the discriminative minimum classification error (MCE) training approach to the problem of learning an appropriate feature space normalization for use with an SVM classifier. Results are presented showing significant error rate reductions for an SVM-based system on a topic identification task using the Fisher corpus of audio recordings of human conversations.
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Summary

The success of support vector machines (SVMs) for classification problems is often dependent on an appropriate normalization of the input feature space. This is particularly true in topic identification, where the relative contribution of the common but uninformative function words can overpower the contribution of the rare but informative content...

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Dialect recognition using adapted phonetic models

Published in:
INTERSPEECH 2008, 22-26 September 2008, p. 763-766.

Summary

In this paper, we introduce a dialect recognition method that makes use of phonetic models adapted per dialect without phonetically labeled data. We show that this method can be implemented efficiently within an existing PRLM system. We compare the performance of this system with other state-of-the-art dialect recognition methods (both acoustic and token-based) on the NIST LRE 2007 English and Mandarin dialect recognition tasks. Our experimental results indicate that this system can perform better than baseline GMM and adapted PRLM systems, and also results in consistent gains of 15-23% when combined with other systems.
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Summary

In this paper, we introduce a dialect recognition method that makes use of phonetic models adapted per dialect without phonetically labeled data. We show that this method can be implemented efficiently within an existing PRLM system. We compare the performance of this system with other state-of-the-art dialect recognition methods (both...

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Eigen-channel compensation and discriminatively trained Gaussian mixture models for dialect and accent recognition

Published in:
Proc. INTERSPEECH 2008, 22-26 September 2008, pp. 723-726.

Summary

This paper presents a series of dialect/accent identification results for three sets of dialects with discriminatively trained Gaussian mixture models and feature compensation using eigen-channel decomposition. The classification tasks evaluated in the paper include: 1)the Chinese language classes, 2) American and Indian accented English and 3) discrimination between three Arabic dialects. The first two tasks were evaluated on the 2007 NIST LRE corpus. The Arabic discrimination task was evaluated using data derived from the LDC Arabic set collected by Appen. Analysis is performed for the English accent problem studied and an approach to open set dialect scoring is introduced. The system resulted in equal error rates at or below 10% for each of the tasks studied.
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Summary

This paper presents a series of dialect/accent identification results for three sets of dialects with discriminatively trained Gaussian mixture models and feature compensation using eigen-channel decomposition. The classification tasks evaluated in the paper include: 1)the Chinese language classes, 2) American and Indian accented English and 3) discrimination between three Arabic...

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