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Dynamic Distributed Dimensional Data Model (D4M) database and computation system

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.
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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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Photonic ADC: overcoming the bottleneck of electronic jitter

Summary

Accurate conversion of wideband multi-GHz analog signals into the digital domain has long been a target of analog-to-digital converter (ADC) developers, driven by applications in radar systems, software radio, medical imaging, and communication systems. Aperture jitter has been a major bottleneck on the way towards higher speeds and better accuracy. Photonic ADCs, which perform sampling using ultra-stable optical pulse trains generated by mode-locked lasers, have been investigated for many years as a promising approach to overcome the jitter problem and bring ADC performance to new levels. This work demonstrates that the photonic approach can deliver on its promise by digitizing a 41 GHz signal with 7.0 effective bits using a photonic ADC built from discrete components. This accuracy corresponds to a timing jitter of 15 fs - a 4-5 times improvement over the performance of the best electronic ADCs which exist today. On the way towards an integrated photonic ADC, a silicon photonic chip with core photonic components was fabricated and used to digitize a 10 GHz signal with 3.5 effective bits. In these experiments, two wavelength channels were implemented, providing the overall sampling rate of 2.1 GSa/s. To show that photonic ADCs with larger channel counts are possible, a dual 20- channel silicon filter bank has been demonstrated.
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Summary

Accurate conversion of wideband multi-GHz analog signals into the digital domain has long been a target of analog-to-digital converter (ADC) developers, driven by applications in radar systems, software radio, medical imaging, and communication systems. Aperture jitter has been a major bottleneck on the way towards higher speeds and better accuracy...

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On-chip nonlinear digital compensation for RF receiver

Published in:
HPEC 2011: Conf. on High Performance Embedded Computing, 21-22 September 2011.

Summary

A system-on-chip (SOC) implementation is an attractive solution for size, weight and power (SWaP) restricted applications, such as mobile devices and UAVs. This is partly because the individual parts of the system can be designed for a specific application rather than for a broad range of them, like commercial parts usually must be. Co-design of the analog hardware and digital processing further enhances the benefits of SOC implementations by allowing, for example, nonlinear digital equalization to further enhance the dynamic range of a given front-end component. This paper presents the implementation of nonlinear digital compensation for an active anti-aliasing filter, which is part of a low-power homodyne receiver design. The RF front-end circuitry and the digital compensation will be integrated in the same chip. Co-design allows the front-end to be designed with known dynamic range limitations that will later be compensated by nonlinear equalization. It also allows nonlinear digital compensation architectures matched to specific circuits and dynamic range requirements--while still maintaining some flexibility to deal with process variation--as opposed to higher power general purpose designs.
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Summary

A system-on-chip (SOC) implementation is an attractive solution for size, weight and power (SWaP) restricted applications, such as mobile devices and UAVs. This is partly because the individual parts of the system can be designed for a specific application rather than for a broad range of them, like commercial parts...

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Sinewave representations of nonmodality

Summary

Regions of nonmodal phonation, exhibiting deviations from uniform glottal-pulse periods and amplitudes, occur often and convey information about speaker- and linguistic-dependent factors. Such waveforms pose challenges for speech modeling, analysis/synthesis, and processing. In this paper, we investigate the representation of nonmodal pulse trains as a sum of harmonically-related sinewaves with time-varying amplitudes, phases, and frequencies. We show that a sinewave representation of any impulsive signal is not unique and also the converse, i.e., frame-based measurements of the underlying sinewave representation can yield different impulse trains. Finally, we argue how this ambiguity may explain addition, deletion, and movement of pulses in sinewave synthesis and a specific illustrative example of time-scale modification of a nonmodal case of diplophonia.
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Summary

Regions of nonmodal phonation, exhibiting deviations from uniform glottal-pulse periods and amplitudes, occur often and convey information about speaker- and linguistic-dependent factors. Such waveforms pose challenges for speech modeling, analysis/synthesis, and processing. In this paper, we investigate the representation of nonmodal pulse trains as a sum of harmonically-related sinewaves with...

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Eigenspace analysis for threat detection in social networks

Published in:
Int. Conf. on Information Fusion, 5 July 2011.

Summary

The problem of detecting a small, anomalous subgraph within a large background network is important and applicable to many fields. The non-Euclidean nature of graph data, however, complicates the application of classical detection theory in this context. A recent statistical framework for anomalous subgraph detection uses spectral properties of a graph's modularity matrix to determine the presence of an anomaly. In this paper, this detection framework and the related algorithms are applied to data focused on a specific application: detection of a threat subgraph embedded in a social network. The results presented use data created to simulate threat activity among noisy interactions. The detectability of the threat subgraph and its separability from the noise is analyzed under a variety of background conditions in both static and dynamic scenarios.
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Summary

The problem of detecting a small, anomalous subgraph within a large background network is important and applicable to many fields. The non-Euclidean nature of graph data, however, complicates the application of classical detection theory in this context. A recent statistical framework for anomalous subgraph detection uses spectral properties of a...

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Anomalous subgraph detection via sparse principal component analysis

Published in:
Proc. 2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, pp. 485-488.

Summary

Network datasets have become ubiquitous in many fields of study in recent years. In this paper we investigate a problem with applicability to a wide variety of domains - detecting small, anomalous subgraphs in a background graph. We characterize the anomaly in a subgraph via the well-known notion of network modularity, and we show that the optimization problem formulation resulting from our setup is very similar to a recently introduced technique in statistics called Sparse Principal Component Analysis (Sparse PCA), which is an extension of the classical PCA algorithm. The exact version of our problem formulation is a hard combinatorial optimization problem, so we consider a recently introduced semidefinite programming relaxation of the Sparse PCA problem. We show via results on simulated data that the technique is very promising.
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Summary

Network datasets have become ubiquitous in many fields of study in recent years. In this paper we investigate a problem with applicability to a wide variety of domains - detecting small, anomalous subgraphs in a background graph. We characterize the anomaly in a subgraph via the well-known notion of network...

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Efficient reconstruction of block-sparse signals

Published in:
IEEE Statistical Signal Processing Workshop, 28-30 June 2011.

Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N > M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved using an l2 optimization with an l1 penalty on the sparsity of the solution. There are practical applications in which multiple measurements can be grouped together, so that K x P data must be estimated from M x P observations, where the l1 sparsity penalty is taken with respect to the vector formed using the l2 norms of the rows of the data matrix. In this paper we develop a computationally efficient block partitioned homotopy method for reconstructing K x P data from M x P observations using a grouped sparsity constraint, and compare its performance to other block reconstruction algorithms.
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Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N > M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved...

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Matched filtering for subgraph detection in dynamic networks

Published in:
2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, pp. 509-512.

Summary

Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic setting a previous method for the detection of anomalously dense subgraphs in a large background. In simulation, we show that this temporal integration technique enables the detection of weak subgraph anomalies than are not detectable in the static case. We also demonstrate background/foreground separation using a real background graph based on a computer network.
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Summary

Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic...

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An active filter achieving 43.6dBm OIP3

Published in:
IEEE Radio Frequency Integrated Circuits Symp., RFIC, 5-7 June 2011.

Summary

An active filter with a 50 omega buffer suitable as an anti-alias filter to drive a highly linear ADC is implemented in 0.13 um SiGe BiCMOS. This 6th-order Chebyshev filter has a 3 dB cutoff frequency of 28.3 MHz and achieves 36.5 dBm OIP3. Nonlinear digital equalization further improves OIP3 to 43.6 dBm. Measurements show 92 dB of rejection at the stopband and a gain of 49 dB. The measured in-band OIP3 of 43.6 dBm is 19 dB higher than previously published designs.
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Summary

An active filter with a 50 omega buffer suitable as an anti-alias filter to drive a highly linear ADC is implemented in 0.13 um SiGe BiCMOS. This 6th-order Chebyshev filter has a 3 dB cutoff frequency of 28.3 MHz and achieves 36.5 dBm OIP3. Nonlinear digital equalization further improves OIP3...

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A time-warping framework for speech turbulence-noise component estimation during aperiodic phonation

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5404-5407.

Summary

The accurate estimation of turbulence noise affects many areas of speech processing including separate modification of the noise component, analysis of degree of speech aspiration for treating pathological voice, the automatic labeling of speech voicing, as well as speaker characterization and recognition. Previous work in the literature has provided methods by which such a high-quality noise component may be estimated in near-periodic speech, but it is known that these methods tend to leak aperiodic phonation (with even slight deviations from periodicity) into the noise-component estimate. In this paper, we improve upon existing algorithms in conditions of aperiodicity by introducing a time-warping based approach to speech noise-component estimation, demonstrating the results on both natural and synthetic speech examples.
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Summary

The accurate estimation of turbulence noise affects many areas of speech processing including separate modification of the noise component, analysis of degree of speech aspiration for treating pathological voice, the automatic labeling of speech voicing, as well as speaker characterization and recognition. Previous work in the literature has provided methods...

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