Publications
External cavity beam combining of 21 semiconductor lasers using SPGD
April 10, 2012
Journal Article
Published in:
Appl. Opt., Vol. 51, No. 11, 10 April 2012, pp. 1724-1728.
Summary
Active coherent beam combining of laser oscillators is an attractive way to achieve high output power in a diffraction limited beam. Here we describe an active beam combining system used to coherently combine 21 semiconductor laser elements with an 81% beam combining efficiency in an external cavity configuration compared with an upper limit of 90% efficiency in the particular configuration of the experiment. Our beam combining system utilizes a stochastic parallel gradient descent (SPGD) algorithm for active phase control. This work demonstrates that active beam combining is not subject to the scaling limits imposed on passive-phasing systems.
Summary
Active coherent beam combining of laser oscillators is an attractive way to achieve high output power in a diffraction limited beam. Here we describe an active beam combining system used to coherently combine 21 semiconductor laser elements with an 81% beam combining efficiency in an external cavity configuration compared with...
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FY11 Line-Supported Bio-Next Program - Multi-modal Early Detection Interactive Classifier (MEDIC) for mild traumatic brain injury (mTBI) triage
April 9, 2012
Project Report
Published in:
MIT Lincoln Laboratory Report LSP-34
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The Multi-modal Early Detection Interactive Classifier (MEDIC) is a triage system designed to enable rapid assessment of mild traumatic brain injury (mTBI) when access to expert diagnosis is limited as in a battlefield setting. MEDIC is based on supervised classification that requires three fundamental components to function correctly; these are data, features, and truth. The MEDIC system can act as a data collection device in addition to being an assessment tool. Therefore, it enables a solution to one of the fundamental challenges in understanding mTBI: the lack of useful data. The vision of MEDIC is to fuse results from stimulus tests in each of four modalitites - auditory, occular, vocal, and intracranial pressure - and provide them to a classifier. With appropriate data for training, the MEDIC classifier is expected to provide an immediate decision of whether the subject has a strong likelihood of having sustained an mTBI and therefore requires an expert diagnosis from a neurologist. The tests within each modalitity were designed to balance the capacity of objective assessment and the maturity of the underlying technology against the ability to distinguish injured from non-injured subjects according to published results. Selection of existing modalities and underlying features represents the best available, low cost, portable technology with a reasonable chance of success.
Summary
The Multi-modal Early Detection Interactive Classifier (MEDIC) is a triage system designed to enable rapid assessment of mild traumatic brain injury (mTBI) when access to expert diagnosis is limited as in a battlefield setting. MEDIC is based on supervised classification that requires three fundamental components to function correctly; these are...
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A scalable signal processing architecture for massive graph analysis
March 25, 2012
Conference Paper
Published in:
ICASSP 2012, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 25-30 March 2012, pp. 5329-32.
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Summary
In many applications, it is convenient to represent data as a graph, and often these datasets will be quite large. This paper presents an architecture for analyzing massive graphs, with a focus on signal processing applications such as modeling, filtering, and signal detection. We describe the architecture, which covers the entire processing chain, from data storage to graph construction to graph analysis and subgraph detection. The data are stored in a new format that allows easy extraction of graphs representing any relationship existing in the data. The principal analysis algorithm is the partial eigendecomposition of the modularity matrix, whose running time is discussed. A large document dataset is analyzed, and we present subgraphs that stand out in the principal eigenspace of the time varying graphs, including behavior we regard as clutter as well as small, tightly-connected clusters that emerge over time.
Summary
In many applications, it is convenient to represent data as a graph, and often these datasets will be quite large. This paper presents an architecture for analyzing massive graphs, with a focus on signal processing applications such as modeling, filtering, and signal detection. We describe the architecture, which covers the...
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Autoregressive HMM speech synthesis
March 25, 2012
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 25-30 March 2012, pp. 4021-4.
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Summary
Autoregressive HMM modeling of spectral features has been proposed as a replacement for standard HMM speech synthesis. The merits of the approach are explored, and methods for enforcing stability of the estimated predictor coefficients are presented. It appears that rather than directly estimating autoregressive HMM parameters, greater synthesis accuracy is obtained by estimating the autoregressive HMM parameters by using a more traditional HMM recognition system to compute state-level posterior probabilities that are then used to accumulate statistics to estimate predictor coefficients. The result is a simplified mathematical framework that requires no modeling of derivatives and still provides smooth synthesis without unnatural spectral discontinuities. The resulting synthesis algorithm involves no matrix solves and may be formulated causally, and appears to result in quality very similar to that of more traditional HMM synthesis approaches. This paper describes the implementation of a complete Autoregressive HMM LVCSR system and its application for synthesis, and describes the preliminary synthesis results.
Summary
Autoregressive HMM modeling of spectral features has been proposed as a replacement for standard HMM speech synthesis. The merits of the approach are explored, and methods for enforcing stability of the estimated predictor coefficients are presented. It appears that rather than directly estimating autoregressive HMM parameters, greater synthesis accuracy is...
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Goodness-of-fit statistics for anomaly detection in Chung-Lu random graphs
March 25, 2012
Conference Paper
Published in:
ICASSP 2012, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 25-30 March 2012, pp. 3265-8.
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Summary
Anomaly detection in graphs is a relevant problem in numerous applications. When determining whether an observation is anomalous with respect to the model of typical behavior, the notion of "goodness of fit" is important. This notion, however, is not well understood in the context of graph data. In this paper, we propose three goodness-of-fit statistics for Chung-Lu random graphs, and analyze their efficacy in discriminating graphs generated by the Chung-Lu model from those with anomalous topologies. In the results of a Monte Carlo simulation, we see that the most powerful statistic for anomaly detection depends on the type of anomaly, suggesting that a hybrid statistic would be the most powerful.
Summary
Anomaly detection in graphs is a relevant problem in numerous applications. When determining whether an observation is anomalous with respect to the model of typical behavior, the notion of "goodness of fit" is important. This notion, however, is not well understood in the context of graph data. In this paper...
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Topic identification based extrinsic evaluation of summarization techniques applied to conversational speech
March 25, 2012
Journal Article
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 25-30 March 2012, pp. 5073-6.
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Summary
Document summarization algorithms are most commonly evaluated according to the intrinsic quality of the summaries they produce. An alternate approach is to examine the extrinsic utility of a summary, measured by the ability of the summary to aid a human in the completion of a specific task. In this paper, we use topic identification as a proxy for relevancy determination in the context of an information retrieval task, and a summary is deemed effective if it enables a user to determine the topical content of a retrieved document. We utilize Amazon's Mechanical Turk service to perform a large-scale human study contrasting four different summarization systems applied to conversational speech from the Fisher Corpus. We show that these results appear to be correlated with the performance of an automated topic identification system, and argue that this automated system can act as a low-cost proxy for a human evaluation during the development stages of a summarization system.
Summary
Document summarization algorithms are most commonly evaluated according to the intrinsic quality of the summaries they produce. An alternate approach is to examine the extrinsic utility of a summary, measured by the ability of the summary to aid a human in the completion of a specific task. In this paper...
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Moments of parameter estimates for Chung-Lu random graph models
March 25, 2012
Conference Paper
Published in:
ICASSP 2012, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 25-30 March 2012, pp. 3961-4.
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Summary
As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non- Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must have access to flexible and tractable statistical models. One model currently in use is the Chung- Lu random graph model, in which edge probabilities are expressed in terms of a given expected degree sequence. An advantage of this model is that its parameters can be obtained via a simple, standard estimator. Although this estimator is used frequently, its statistical properties have not been fully studied. In this paper, we develop a central limit theory for a simplified version of the Chung-Lu parameter estimator. We then derive approximations for moments of the general estimator using the delta method, and confirm the effectiveness of these approximations through empirical examples.
Summary
As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non- Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must have access to flexible and tractable statistical models. One...
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Dynamic Distributed Dimensional Data Model (D4M) database and computation system
March 25, 2012
Conference Paper
Published in:
ICASSP 2012, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 25-30 March 2012, pp. 5349-52.
Summary
A crucial element of large web companies is their ability to collect and analyze massive amounts of data. Tuple store databases are a key enabling technology employed by many of these companies (e.g., Google Big Table and Amazon Dynamo). Tuple stores are highly scalable and run on commodity clusters, but lack interfaces to support efficient development of mathematically based analytics. D4M (Dynamic Distributed Dimensional Data Model) has been developed to provide a mathematically rich interface to tuple stores (and structured query language "SQL" databases). D4M allows linear algebra to be readily applied to databases. Using D4M, it is possible to create composable analytics with significantly less effort than using traditional approaches. This work describes the D4M technology and its application and performance.
Summary
A crucial element of large web companies is their ability to collect and analyze massive amounts of data. Tuple store databases are a key enabling technology employed by many of these companies (e.g., Google Big Table and Amazon Dynamo). Tuple stores are highly scalable and run on commodity clusters, but...
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Design and analysis of a hyperspectral microwave receiver subsystem
March 9, 2012
Conference Paper
Published in:
MICRORAD 2012, 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, 5-9 March 2012.
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Summary
Recent technology advances have profoundly changed the landscape of modern radiometry by enabling miniaturized, low-power, and low-noise radio-frequency receivers operating at frequencies near 200 GHz and beyond. These advances enable the practical use of receiver arrays to multiplex multiple broad frequency bands into many spectral channels. We use the term "hyperspectral microwave" to refer generically to microwave sounding systems with approximately 50 spectral channels or more. In this paper, we report on the design and analysis of the receiver subsystem (lensed antenna, RF frontend electronics, and IF processor module) for the Hyperspectral Microwave Atmospheric Sounder (HyMAS) comprising multiple receivers near the oxygen absorption line at 118.75 GHz and the water vapor absorption line at 183.31 GHz. The hyperspectral microwave receiver system will be integrated into a new scanhead compatible with the NASA GSFC Conical Scanning Microwave Imaging Radiometer/Compact Submillimeter-wave Imaging Radiometer (CoSMIR/CoSSIR) airborne instrument system to facilitate demonstration and performance characterization under funding from the NASA ESTO Advanced Component Technology program. Four identical radiometers will be used to cover 108-119 GHz, and two identical receivers will be used to cover 173-183 GHz. Subharmonic mixers will be driven by frequency-multiplied dielectric resonant oscillators, and single-sideband operation will be achieved by waveguide filtering of the lower sideband. A relatively high IF frequency is chosen to facilitate miniaturization of the IF processor module, which will be fabricated using Low Temperature Co-fired Ceramic (LTCC) technology. Corrugated feed antennas with lenses are used to achieve a FWHM beamwidth of approximately 3.5 degrees. Two polarizations are measured by each feed to increase overall channel count, and multiple options will be considered during the design phase for the polarization diplexing approach. Broadband operation over a relatively high intermediate frequency range (18-29 GHz) is a technical challenge of the front-end receiver systems, and a receiver temperature of approximately 2000-3000K is expected over the receiver bandwidth. This performance, together with approximately l00-msec integration times typical of airborne operation, yields channel NEDTs of approximately 0.35K, which is adequate to demonstrate the hyperspectral microwave concept by comparing profile retrievals with high-fidelity ground truth available either by coincident overpasses of hyperspectral infrared sounders and/or in situ radiosonde/dropsonde measurements.
Summary
Recent technology advances have profoundly changed the landscape of modern radiometry by enabling miniaturized, low-power, and low-noise radio-frequency receivers operating at frequencies near 200 GHz and beyond. These advances enable the practical use of receiver arrays to multiplex multiple broad frequency bands into many spectral channels. We use the term...
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Hazard alerting based on probabilistic models
March 1, 2012
Journal Article
Published in:
J. Guidance, Control, Dynamics, Vol. 35, No. 2, March-April 2012, pp. 442-450.
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Hazard alerting systems alert operators to potential future undesirable events so that action may be taken to mitigate risk. One way to develop a hazard alerting system based on probabilistic models is by using a threshold-based approach, where the probability of the undesirable event without mitigation is compared against a threshold. Another way to develop such a system is to model the system as a Markov decision process and solve for the hazard experiments reveal that an expected utility approach performs better than threshold-based approaches when the dynamic stochasticity is high, where accounting for delays or changes in the alert becomes more important. however, for certain system parameters and operating environments, a threshold-based approach may provide comparable performance.
Summary
Hazard alerting systems alert operators to potential future undesirable events so that action may be taken to mitigate risk. One way to develop a hazard alerting system based on probabilistic models is by using a threshold-based approach, where the probability of the undesirable event without mitigation is compared against a...
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