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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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I-vector speaker and language recognition system on Android

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

Summary

I-Vector based speaker and language identification provides state of the art performance. However, this comes as a more computationally complex solution, which can often lead to challenges in resource-limited devices, such as phones or tablets. We present the implementation of an I-Vector speaker and language recognition system on the Android platform in the form of a fully functional application that allows speaker enrollment and language/speaker scoring within mobile contexts. We include a detailed account of the challenges to port the system and its dependencies, which were necessary to optimize matrix operations in the I-Vector implementation. The system was benchmarked on a for a Google Nexus 6, showing a speed increase of 61.68% in scoring and 82.63% in enrollment operations with the implemented optimizations. The application was tested in mobile settings on a Nexus 7 tablet with forty participants, showing a rough accuracy of 84%. The optimized platform showed the capacity to perform near real-time recognition within a mobile setting and showcases the viability of I-Vector systems on resource-limited environments.
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Summary

I-Vector based speaker and language identification provides state of the art performance. However, this comes as a more computationally complex solution, which can often lead to challenges in resource-limited devices, such as phones or tablets. We present the implementation of an I-Vector speaker and language recognition system on the Android...

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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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Sparse-coded net model and applications

Published in:
2016 IEEE Int. Workshop on Machine Learning for Signal Processing, 13-16 September 2016.

Summary

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task perform well, we argue that sparse coding should also be built as a holistic learning unit optimizing on the supervised task objectives more explicitly. In this paper, we propose sparse-coded net, a feedforward model that integrates sparse coding and task-driven output layers, and describe training methods in detail. After pretraining a sparse-coded net via semi-supervised learning, we optimize its task-specific performance in a novel backpropagation algorithm that can traverse nonlinear feature pooling operators to update the dictionary. Thus, sparse-coded net can be applied to supervised dictionary learning. We evaluate sparse-coded net with classification problems in sound, image, and text data. The results confirm a significant improvement over semi-supervised learning as well as superior classification performance against deep stacked autoencoder neural network and GMM-SVM pipelines in small to medium-scale settings.
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Summary

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task...

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Designing a new high performance computing education strategy for professional scientists and engineers

Summary

For decades the High Performance Computing (HPC) community has used web content, workshops and embedded HPC scientists to enable practitioners to harness the power of parallel and distributed computing. The most successful approaches, face-to-face tutorials and embedded professionals, don't scale. To create scalable, flexible, educational experiences for practitioners in all phases of a career, from student to professional, we turn to Massively Open Online Courses (MOOCs). We detail the conversion of personalized tutorials to a selfpaced online course. In this demonstration, we highlight a course that mimics in-person tutorials by providing personalized paths through content that interleaves theory and practice, to help researchers learn key parallel computing concepts while developing familiarity with their HPC target system.
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Summary

For decades the High Performance Computing (HPC) community has used web content, workshops and embedded HPC scientists to enable practitioners to harness the power of parallel and distributed computing. The most successful approaches, face-to-face tutorials and embedded professionals, don't scale. To create scalable, flexible, educational experiences for practitioners in all...

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From NoSQL Accumulo to NewSQL Graphulo: design and utility of graph algorithms inside a BigTable database

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

Summary

Google BigTable's scale-out design for distributed key-value storage inspired a generation of NoSQL databases. Recently the NewSQL paradigm emerged in response to analytic workloads that demand distributed computation local to data storage. Many such analytics take the form of graph algorithms, a trend that motivated the GraphBLAS initiative to standardize a set of matrix math kernels for building graph algorithms. In this article we show how it is possible to implement the GraphBLAS kernels in a BigTable database by presenting the design of Graphulo, a library for executing graph algorithms inside the Apache Accumulo database. We detail the Graphulo implementation of two graph algorithms and conduct experiments comparing their performance to two main-memory matrix math systems. Our results shed insight into the conditions that determine when executing a graph algorithm is faster inside a database versus an external system—in short, that memory requirements and relative I/O are critical factors.
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Summary

Google BigTable's scale-out design for distributed key-value storage inspired a generation of NoSQL databases. Recently the NewSQL paradigm emerged in response to analytic workloads that demand distributed computation local to data storage. Many such analytics take the form of graph algorithms, a trend that motivated the GraphBLAS initiative to standardize...

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