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Crosstalk characterization and mitigation in Geiger-mode avalanche photodiode arrays

Summary

Intra focal plane array (FPA) crosstalk is a primary development limiter of large, fine-pixel Geiger-mode avalanche photodiode (Gm-APD) arrays beyond 256×256 pixels. General analysis methods and results from MIT Lincoln Laboratory (MIT/LL) InP-based detector arrays will be presented.
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Summary

Intra focal plane array (FPA) crosstalk is a primary development limiter of large, fine-pixel Geiger-mode avalanche photodiode (Gm-APD) arrays beyond 256×256 pixels. General analysis methods and results from MIT Lincoln Laboratory (MIT/LL) InP-based detector arrays will be presented.

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Biomimetic antenna array using non-foster network to enhance directional sensitivity over broad frequency band

Published in:
IEEE Trans. Antennas Propag., Vol. 64, No. 10, October 2016, pp. 4297-4305.

Summary

Biologically inspired antenna arrays that mimic the hearing mechanism of insects are called biomimetic antenna arrays (BMAAs). They are attractive for microwave applications, such as compact direction finding systems. Earlier, the BMAAs were designed for narrow frequency band phase enhancement, whereas we now propose to design them for use with a non-Foster coupling network (NFC). As the NFCs are not restricted by gain bandwidth product, their incorporation in the design can provide wideband phase enhancement. A method for designing BMAA, using a non-Foster coupling network (NFC-BMAA), and also for obtaining system stability, is presented. Simulated and measured results of the fabricated structure are also presented and discussed.
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Summary

Biologically inspired antenna arrays that mimic the hearing mechanism of insects are called biomimetic antenna arrays (BMAAs). They are attractive for microwave applications, such as compact direction finding systems. Earlier, the BMAAs were designed for narrow frequency band phase enhancement, whereas we now propose to design them for use with...

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Side channel authenticity discriminant analysis for device class identification

Summary

Counterfeit microelectronics present a significant challenge to commercial and defense supply chains. Many modern anti-counterfeit strategies rely on manufacturer cooperation to include additional identification components. We instead propose Side Channel Authenticity Discriminant Analysis (SICADA) to leverage physical phenomena manifesting from device operation to match suspect parts to a class of authentic parts. This paper examines the extent that power dissipation information can be used to separate unique classes of devices. A methodology for distinguishing device types is presented and tested on both simulation data of a custom circuit and empirical measurements of Microchip dsPIC33F microcontrollers. Experimental results show that power side channels contain significant distinguishing information to identify parts as authentic or suspect counterfeit.
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Summary

Counterfeit microelectronics present a significant challenge to commercial and defense supply chains. Many modern anti-counterfeit strategies rely on manufacturer cooperation to include additional identification components. We instead propose Side Channel Authenticity Discriminant Analysis (SICADA) to leverage physical phenomena manifesting from device operation to match suspect parts to a class of...

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How deep neural networks can improve emotion recognition on video data

Published in:
ICIP: 2016 IEEE Int. Conf. on Image Processing, 25-28 September 2016.

Summary

We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance. We present our findings on videos from the Audio/Visual+Emotion Challenge (AV+EC2015). In our experiments, we analyze the effects of several hyperparameters on overall performance while also achieving superior performance to the baseline and other competing methods.
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Summary

We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this...

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The Offshore Precipitation Capability

Summary

In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and several fields from the Rapid Refresh (RAP) 13 km numerical weather prediction model to create precipitation and echo top fields similar to those provided by existing Federal Aviation Administration (FAA) weather systems. Preprocessing and feature extraction methods are described to construct inputs for model training. A variety of machine learning algorithms are investigated to identify which provides the most accuracy. Output from the machine learning model is blended with existing radar mosaics to create weather radar-like analyses that extend into offshore regions. The resulting fields are validated using land radars and satellite precipitation measurements provided by the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) core observatory satellite. This capability is initially being developed for the Miami Oceanic airspace with the goal of providing improved situational awareness for offshore air traffic control.
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Summary

In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and...

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Enhancing HPC security with a user-based firewall

Summary

High Performance Computing (HPC) systems traditionally allow their users unrestricted use of their internal network. While this network is normally controlled enough to guarantee privacy without the need for encryption, it does not provide a method to authenticate peer connections. Protocols built upon this internal network, such as those used in MPI, Lustre, Hadoop, or Accumulo, must provide their own authentication at the application layer. Many methods have been employed to perform this authentication, such as operating system privileged ports, Kerberos, munge, TLS, and PKI certificates. However, support for all of these methods requires the HPC application developer to include support and the user to configure and enable these services. The user-based firewall capability we have prototyped enables a set of rules governing connections across the HPC internal network to be put into place using Linux netfilter. By using an operating system-level capability, the system is not reliant on any developer or user actions to enable security. The rules we have chosen and implemented are crafted to not impact the vast majority of users and be completely invisible to them. Additionally, we have measured the performance impact of this system under various workloads.
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Summary

High Performance Computing (HPC) systems traditionally allow their users unrestricted use of their internal network. While this network is normally controlled enough to guarantee privacy without the need for encryption, it does not provide a method to authenticate peer connections. Protocols built upon this internal network, such as those used...

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High-throughput ingest of data provenance records in Accumulo

Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.

Summary

Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must be stored somewhere where it can be queried. This problem becomes even more challenging when considering a network of provenance-aware machines all collecting this metadata. In this paper, we investigate the use of D4M and Accumulo to support high-throughput data ingest of whole-system provenance data. We find that we are able to ingest 3,970 graph components per second. Centrally storing the provenance metadata allows us to build systems that can detect and respond to data integrity attacks that are captured by the provenance system.
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Summary

Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must...

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HF vector sensor for radio astronomy: ground testing results

Summary

The radio sky below ~10 MHz is largely unexplored due to the inability of ground-based telescopes to observe near or below the ionospheric plasma frequency, or cut-off frequency. A space-based interferometric array is required to probe the portion of the electromagnetic (E-M) spectrum below 10 MHz with sufficient angular resolution and sensitivity to be scientifically useful. Multi-spacecraft constellations scale quickly in cost and complexity as the number of spacecraft increases, so minimizing the number of required spacecraft for an interferometric array (while maintaining performance) is critical for feasibility. We present the HF (High Frequency, 3 to 30 MHz) Vector Sensor as a high performance spacecraft instrument in a future space-based interferometric array. The HF Vector Sensor is composed of three orthogonal dipoles and three orthogonal loop antennas with a common phase center. These six elements fully measure the E-M field of incoming radiation. We present the design of two prototype HF Vector Sensors, ground-based data collection at frequencies above the ionospheric cut-off, and imaging results using several different algorithms.
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Summary

The radio sky below ~10 MHz is largely unexplored due to the inability of ground-based telescopes to observe near or below the ionospheric plasma frequency, or cut-off frequency. A space-based interferometric array is required to probe the portion of the electromagnetic (E-M) spectrum below 10 MHz with sufficient angular resolution...

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Benchmarking the Graphulo processing framework

Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.

Summary

Graph algorithms have wide applicability to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS are designed to specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS kernels in the Apache Accumulo database. In our previous work, we have demonstrated a core Graphulo operation that performs large scale multiplication operations of database tables called TableMult. In this article, we present results of scaling the Graphulo engine to larger problems and scalablity when using greater number of resources. Specifically, we present the results of two experiments that demonstrate Graphulo scaling performance as linear with the number of available resources. The first experiment demonstrates cluster processing rates through Graphulo's TableMult operator on two large graphs, scaled between 2^17 and 2^19 vertices. The second experiment uses TableMult to extract a random set of rows from a large graph (2^19 nodes) to simulate a cued graph analytic. These benchmarking results are of relevance to Graphulo users who wish to apply Graphulo to their graph problems.
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Summary

Graph algorithms have wide applicability to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS are designed to specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS...

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In-storage embedded accelerator for sparse pattern processing

Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.

Summary

We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable of handling up to 1TB of data, and experiments show that it can outperform C/C++ software solutions on a 16-core system at a fraction of the power and cost; an optimized version of the accelerator can match the performance of a 48-core server.
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Summary

We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable...

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