Publications
Model of turn-on characteristics of InP-based Geiger-mode avalanche photodiodes suitable for circuit simulations
April 22, 2015
Conference Paper
Published in:
SPIE, Vol. 9492, Advanced Photon Counting Techniques IX, 28 May 2015.
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Summary
A model for the turn-on characteristics of separate-absorber-multiplier InP-based Geiger-mode Avalanche Photodiodes (APDs) has been developed. Verilog-A was used to implement the model in a manner that can be incorporated into circuit simulations. Rather than using SPICE elements to mimic the voltage and current characteristics of the APD, Verilog-A can represent the first order nonlinear differential equations that govern the avalanche current of the APD. This continuous time representation is fundamentally different than the piecewise linear characteristics of other models. The model is based on a driving term for the differential current, which is given by the voltage overbias minus the voltage drop across the device?s space-charge resistance RSC. This drop is primarily due to electrons transiting the separate absorber. RSC starts off high and decreases with time as the initial breakdown filament spreads laterally to fill the APD. With constant bias voltage, the initial current grows exponentially until space charge effects reduce the driving function. With increasing current the driving term eventually goes to zero and the APD current saturates. On the other hand, if the APD is biased with a capacitor, the driving term becomes negative as the capacitor discharges, reducing the current and driving the voltage below breakdown. The model parameters depend on device design and are obtained from fitting the model to Monte-Carlo turn-on simulations that include lateral spreading of the carriers of the relevant structure. The Monte-Carlo simulations also provide information on the probability of avalanche, and jitter due to where the photon is absorbed in the APD.
Summary
A model for the turn-on characteristics of separate-absorber-multiplier InP-based Geiger-mode Avalanche Photodiodes (APDs) has been developed. Verilog-A was used to implement the model in a manner that can be incorporated into circuit simulations. Rather than using SPICE elements to mimic the voltage and current characteristics of the APD, Verilog-A can...
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Deep neural network approaches to speaker and language recognition
April 21, 2015
Journal Article
Published in:
IEEE Signal Process. Lett., Vol. 22, No. 10, October 2015, pp. 1671-5.
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The impressive gains in performance obtained using deep neural networks (DNNs) for automatic speech recognition (ASR) have motivated the application of DNNs to other speech technologies such as speaker recognition (SR) and language recognition (LR). Prior work has shown performance gains for separate SR and LR tasks using DNNs for direct classification or for feature extraction. In this work we present the application for single DNN for both SR and LR using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks. Using a single DNN trained for ASR on Switchboard data we demonstrate large gains on performance in both benchmarks: a 55% reduction in EER for the DAC13 out-of-domain condition and a 48% reduction in Cavg on the LRE11 30 s test condition. It is also shown that further gains are possible using score or feature fusion leading to the possibility of a single i-vector extractor producing state-of-the-art SR and LR performance.
Summary
The impressive gains in performance obtained using deep neural networks (DNNs) for automatic speech recognition (ASR) have motivated the application of DNNs to other speech technologies such as speaker recognition (SR) and language recognition (LR). Prior work has shown performance gains for separate SR and LR tasks using DNNs for...
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Planted clique detection below the noise floor using low-rank sparse PCA
April 19, 2015
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 19-24 April 2015.
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Detection of clusters and communities in graphs is useful in a wide range of applications. In this paper we investigate the problem of detecting a clique embedded in a random graph. Recent results have demonstrated a sharp detectability threshold for a simple algorithm based on principal component analysis (PCA). Sparse PCA of the graph's modularity matrix can successfully discover clique locations where PCA-based detection methods fail. In this paper, we demonstrate that applying sparse PCA to low-rank approximations of the modularity matrix is a viable solution to the planted clique problem that enables detection of small planted cliques in graphs where running the standard semidefinite program for sparse PCA is not possible.
Summary
Detection of clusters and communities in graphs is useful in a wide range of applications. In this paper we investigate the problem of detecting a clique embedded in a random graph. Recent results have demonstrated a sharp detectability threshold for a simple algorithm based on principal component analysis (PCA). Sparse...
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Quantitative evaluation of moving target technology
April 14, 2015
Conference Paper
Published in:
HST 2015, IEEE Int. Symp. on Technologies for Homeland Security, 14-16 April 2015.
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Summary
Robust, quantitative measurement of cyber technology is critically needed to measure the utility, impact and cost of cyber technologies. Our work addresses this need by developing metrics and experimental methodology for a particular type of technology, moving target technology. In this paper, we present an approach to quantitative evaluation, including methodology and metrics, results of analysis, simulation and experiments, and a series of lessons learned.
Summary
Robust, quantitative measurement of cyber technology is critically needed to measure the utility, impact and cost of cyber technologies. Our work addresses this need by developing metrics and experimental methodology for a particular type of technology, moving target technology. In this paper, we present an approach to quantitative evaluation, including...
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Robust face recognition-based search and retrieval across image stills and video
April 14, 2015
Conference Paper
Published in:
HST 2015, IEEE Int. Symp. on Technologies for Homeland Security, 14-16 April 2015.
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Summary
Significant progress has been made in addressing face recognition channel, sensor, and session effects in both still images and video. These effects include the classic PIE (pose, illumination, expression) variation, as well as variations in other characteristics such as age and facial hair. While much progress has been made, there has been little formal work in characterizing and compensating for the intrinsic differences between faces in still images and video frames. These differences include that faces in still images tend to have neutral expressions and frontal poses, while faces in videos tend to have more natural expressions and poses. Typically faces in videos are also blurrier, have lower resolution, and are framed differently than faces in still images. Addressing these issues is important when comparing face images between still images and video frames. Also, face recognition systems for video applications often rely on legacy face corpora of still images and associated meta data (e.g. identifying information, landmarks) for development, which are not formally compensated for when applied to the video domain. In this paper we will evaluate the impact of channel effects on face recognition across still images and video frames for the search and retrieval task. We will also introduce a novel face recognition approach for addressing the performance gap across these two respective channels. The datasets and evaluation protocols from the Labeled Faces in the Wild (LFW) still image and YouTube Faces (YTF) video corpora will be used for the comparative characterization and evaluation. Since the identities of subjects in the YTF corpora are a subset of those in the LFW corpora, this enables an apples-to-apples comparison of in-corpus and cross-corpora face comparisons.
Summary
Significant progress has been made in addressing face recognition channel, sensor, and session effects in both still images and video. These effects include the classic PIE (pose, illumination, expression) variation, as well as variations in other characteristics such as age and facial hair. While much progress has been made, there...
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Global pattern search at scale
April 14, 2015
Conference Paper
Published in:
IEEE Int. Symp. on Technologies for Homeland Security, 14-16 April 2015.
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Summary
In recent years, data collection has far outpaced the tools for data analysis in the area of non-traditional GEOINT analysis. Traditional tools are designed to analyze small-scale numerical data, but there are few good interactive tools for processing large amounts of unstructured data such as raw text. In addition to the complexities of data processing, presenting the data in a way that is meaningful to the end user poses another challenge. In our work, we focused on analyzing a corpus of 35,000 news articles and creating an interactive geovisualization tool to reveal patterns to human analysts. Our comprehensive tool, Global Pattern Search at Scale (GPSS), addresses three major problems in data analysis: free text analysis, high volumes of data, and interactive visualization. GPSS uses an Accumulo database for high-volume data storage, and a matrix of word counts and event detection algorithms to process the free text. For visualization, the tool displays an interactive web application to the user, featuring a map overlaid with document clusters and events, search and filtering options, a timeline, and a word cloud. In addition, the GPSS tool can be easily adapted to process and understand other large free-text datasets.
Summary
In recent years, data collection has far outpaced the tools for data analysis in the area of non-traditional GEOINT analysis. Traditional tools are designed to analyze small-scale numerical data, but there are few good interactive tools for processing large amounts of unstructured data such as raw text. In addition to...
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Computing on Masked Data to improve the security of big data
April 14, 2015
Conference Paper
Published in:
HST 2015, IEEE Int. Conf. on Technologies for Homeland Security, 14-16 April 2015.
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Summary
Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such untrusted servers or databases. Advances in cryptographic techniques and database technologies provide the necessary security functionality but rely on a computational model in which the cloud is used solely for storage and retrieval. Much of big data computation and analytics make use of signal processing fundamentals for computation. As the trend of moving data storage and computation to the cloud increases, homeland security missions should understand the impact of security on key signal processing kernels such as correlation or thresholding. In this article, we propose a tool called Computing on Masked Data (CMD), which combines advances in database technologies and cryptographic tools to provide a low overhead mechanism to offload certain mathematical operations securely to the cloud. This article describes the design and development of the CMD tool.
Summary
Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such...
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Rapid sequence identification of potential pathogens using techniques from sparse linear algebra
April 14, 2015
Conference Paper
Published in:
HST 2015, IEEE Int. Conf. on Technologies for Homeland Security, 14-16 April 2015.
Summary
The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D4RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling and computational power of the Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear algebra and statistical properties to increase computational performance while retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield speed and precision tradeoffs, with applications in biodefense and medical diagnostics. The D4RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest.
Summary
The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D4RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling...
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Agent-based simulation for assessing network security risk due to unauthorized hardware
April 12, 2015
Conference Paper
Published in:
SpringSim 2015: Spring Simulation Multiconference, 12-15 April 2015.
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Summary
Computer networks are present throughout all sectors of our critical infrastructure and these networks are under a constant threat of cyber attack. One prevalent computer network threat takes advantage of unauthorized, and thus insecure, hardware on a network. This paper presents a prototype simulation system for network risk assessment that is intended for use by administrators to simulate and evaluate varying network environments and attacker/defender scenarios with respect to authorized and unauthorized hardware. The system is built on the agent-based modeling paradigm and captures emergent system dynamics that result from the interactions of multiple network agents including regular and administrator users, attackers, and defenders in a network environment. The agent-based system produces both metrics and visualizations that provide insights into network security risk and serve to guide the search for efficient policies and controls to protect a network from attacks related to unauthorized hardware. The simulation model is unique in the current literature both for its network threat model and its visualized agent-based approach. We demonstrate the model via a case study that evaluates risk for several candidate security policies on a representative computer network.
Summary
Computer networks are present throughout all sectors of our critical infrastructure and these networks are under a constant threat of cyber attack. One prevalent computer network threat takes advantage of unauthorized, and thus insecure, hardware on a network. This paper presents a prototype simulation system for network risk assessment that...
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Broadband magnetometry and temperature sensing with a light-trapping diamond waveguide
April 6, 2015
Journal Article
Published in:
Nature Phys. Lett., Vol. 11, May 2015, pp. 393-7.
Summary
Solid-state quantum sensors are attracting wide interest because of their sensitivity at room temperature. In particular, the spin properties of individual nitrogen-vacancy (NV) colour centres in diamond make them outstanding nanoscale sensors of magnetic fields, electric fields and temperature under ambient conditions. Recent work on NV ensemble-based magnetometers, inertial sensors, and clocks has employed unentangled colour centres to realize significant improvements in sensitivity. However, to achieve this potential sensitivity enhancement in practice, new techniques are required to excite efficiently and to collect the optical signal from large NV ensembles. Here, we introduce a light-trapping diamond waveguide geometry with an excitation efficiency and signal collection that enables in excess of 5% conversion efficiency of pump photons into optically detected magnetic resonance (ODMR) fluorescence--an improvement over previous single-pass geometries of more than three orders of magnitude. This marked enhancement of the ODMR signal enables precision broadband measurements of magnetic field and temperature in the low-frequency range, otherwise inaccessible by dynamical decoupling techniques.
Summary
Solid-state quantum sensors are attracting wide interest because of their sensitivity at room temperature. In particular, the spin properties of individual nitrogen-vacancy (NV) colour centres in diamond make them outstanding nanoscale sensors of magnetic fields, electric fields and temperature under ambient conditions. Recent work on NV ensemble-based magnetometers, inertial sensors...
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