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A new radio frequency interference filter for weather radars
July 1, 2017
Journal Article
Published in:
J. Atmos. Ocean. Technol., Vol. 34, No. 7, 1 July 2017, pp. 1393-1406.
Summary
A new radio frequency interference (RFI) filter algorithm for weather radars is proposed in the two-dimensional (2D) range-time/sample-time domain. Its operation in 2D space allows RFI detection at lower interference-to-noise or interference-to-signal ratios compared to filters working only in the sample-time domain while maintaining very low false alarm rates. Simulations and real weather radar data with RFI are used to perform algorithm comparisons. Results are consistent with theoretical considerations and show the 2D RFI filter to be a promising addition to the signal processing arsenal against interference with weather radars. Increased computational burden is the only drawback relative to filters currently used by operational systems.
Summary
A new radio frequency interference (RFI) filter algorithm for weather radars is proposed in the two-dimensional (2D) range-time/sample-time domain. Its operation in 2D space allows RFI detection at lower interference-to-noise or interference-to-signal ratios compared to filters working only in the sample-time domain while maintaining very low false alarm rates. Simulations...
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Fabrication security and trust of domain-specific ASIC processors
May 1, 2017
Conference Paper
Published in:
IEEE Intl. Symp. on Hardware Oriented Security and Trust, HOST, 1-5 May 2017.
Summary
Application specific integrated circuits (ASICs) are commonly used to implement high-performance signal-processing systems for high-volume applications, but their high development costs and inflexible nature make ASICs inappropriate for algorithm development and low-volume DoD applications. In addition, the intellectual property (IP) embedded in the ASIC is at risk when fabricated in an untrusted foundry. Lincoln Laboratory has developed a flexible signal-processing architecture to implement a wide range of algorithms within one application domain, for example radar signal processing. In this design methodology, common signal processing kernels such as digital filters, fast Fourier transforms (FFTs), and matrix transformations are implemented as optimized modules, which are interconnected by a programmable wiring fabric that is similar to the interconnect in a field programmable gate array (FPGA). One or more programmable microcontrollers are also embedded in the fabric to sequence the operations. This design methodology, which has been termed a coarse-grained FPGA, has been shown to achieve a near ASIC level of performance. In addition, since the signal processing algorithms are expressed in firmware that is loaded at runtime, the important application details are protected from an unscrupulous foundry.
Summary
Application specific integrated circuits (ASICs) are commonly used to implement high-performance signal-processing systems for high-volume applications, but their high development costs and inflexible nature make ASICs inappropriate for algorithm development and low-volume DoD applications. In addition, the intellectual property (IP) embedded in the ASIC is at risk when fabricated in...
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Intersection and convex combination in multi-source spectral planted cluster detection
December 7, 2016
Conference Paper
Published in:
IEEE Global Conf. on Signal and Information Processing, GlobalSIP, 7-9 December 2016.
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Summary
Planted cluster detection is an important form of signal detection when the data are in the form of a graph. When there are multiple graphs representing multiple connection types, the method of aggregation can have significant impact on the results of a detection algorithm. This paper addresses the tradeoff between two possible aggregation methods: convex combination and intersection. For a spectral detection method, convex combination dominates when the cluster is relatively sparse in at least one graph, while the intersection method dominates in cases where it is dense across graphs. Experimental results confirm the theory. We consider the context of adversarial cluster placement, and determine how an adversary would distribute connections among the graphs to best avoid detection.
Summary
Planted cluster detection is an important form of signal detection when the data are in the form of a graph. When there are multiple graphs representing multiple connection types, the method of aggregation can have significant impact on the results of a detection algorithm. This paper addresses the tradeoff between...
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Simultaneous transmit and receive with digital phased arrays
October 18, 2016
Conference Paper
Published in:
6th Int. Symp. on Phased Array Systems and Technology, PAST 2016, 18-21 October 2016.
Summary
A new architecture is proposed for achieving Simultaneous Transmit and Receive (STAR) with a digital phased array. We demonstrate how digital beamforming and cancellation enables adjacent transmitting and receiving sub-arrays to operate simultaneously in the same frequency band without a significant reduction in performance. Our approach uses only digital signal processing techniques and does not require custom radiators or analog cancelling circuits that can increase front-end losses and add significant size, weight and cost to the array. Simulated results are presented for a 50-element array that achieves more than 160 dB of effective isolation between transmit and receive beams over a 100 MHz instantaneous band centered at 2.45 GHz.
Summary
A new architecture is proposed for achieving Simultaneous Transmit and Receive (STAR) with a digital phased array. We demonstrate how digital beamforming and cancellation enables adjacent transmitting and receiving sub-arrays to operate simultaneously in the same frequency band without a significant reduction in performance. Our approach uses only digital signal...
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Scalable prototyping testbed for MMW imager system
October 18, 2016
Conference Paper
Published in:
6th Int. Symp. on Phased Array Systems and Technology, PAST 2016, 18-21 October 2016.
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Summary
A prototyping testbed for an experimental millimeter-wave multiple-imput multiple-output (MIMO) radar system for security applications in high foot-traffic areas will be presented. The system is designed for flexible operation at a 10 Hz video rate, enabled by high-speed electronic scanning and real-time signal processing. Overall imaging system costs are reduced by the use of an innovative ultra-sparse multistatic radar solution and a 3-D near-field beamforming image construction technique targeted for low-cost high-throughput GPU processors. The testbed is architected with FPGAs, GPUs, CPU storage, and networking, capable of supporting future growth in capabilities, such as interference suppression & advanced signal processing algorithms, auxiliary sensing modalities, near-sensor analytics, and integration into a system-of-systems security architecture.
Summary
A prototyping testbed for an experimental millimeter-wave multiple-imput multiple-output (MIMO) radar system for security applications in high foot-traffic areas will be presented. The system is designed for flexible operation at a 10 Hz video rate, enabled by high-speed electronic scanning and real-time signal processing. Overall imaging system costs are reduced...
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Multi-modal audio, video and physiological sensor learning for continuous emotion prediction
October 16, 2016
Conference Paper
Published in:
ACM Multimedia Conf. AVEC Workshop, 15-19 October 2016.
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Summary
The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. The Audio Video Emotion Challenge (AVEC) 2016 provides a well-defined framework for developing and rigorously evaluating innovative approaches for estimating the arousal and valence states of emotion as a function of time. It presents the opportunity for investigating multimodal solutions that include audio, video, and physiological sensor signals. This paper provides an overview of our AVEC Emotion Challenge system, which uses multi-feature learning and fusion across all available modalities. It includes a number of technical contributions, including the development of novel high- and low-level features for modeling emotion in the audio, video, and physiological channels. Low-level features include modeling arousal in audio with minimal prosodic-based descriptors. High-level features are derived from supervised and unsupervised machine learning approaches based on sparse coding and deep learning. Finally, a state space estimation approach is applied for score fusion that demonstrates the importance of exploiting the time-series nature of the arousal and valence states. The resulting system outperforms the baseline systems [10] on the test evaluation set with an achieved Concordant Correlation Coefficient (CCC) for arousal of 0.770 vs 0.702 (baseline) and for valence of 0.687 vs 0.638. Future work will focus on exploiting the time-varying nature of individual channels in the multi-modal framework.
Summary
The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. The Audio Video Emotion Challenge (AVEC) 2016 provides a well-defined framework for developing and rigorously evaluating innovative approaches for estimating the...
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Detecting depression using vocal, facial and semantic communication cues
October 15, 2016
Conference Paper
Published in:
ACM Multimedia Conf. AVEC Workshop, 15-19 October 2016.
Summary
Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In particular, MDD-induced neurophysiological changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities, drawing first from previously developed neurophysiologically motivated speech and facial coordination and timing features. In addition, a novel indicator of lower vocal tract constriction in articulation is incorporated that relates to vocal projection. Semantic features are analyzed for subject/avatar dialogue content using a sparse coded lexical embedding space, and for contextual clues related to the subject's present or past depression status. The features and depression classification system were developed for the 6th International Audio/Video Emotion Challenge (AVEC), which provides data consisting of audio, video-based facial action units, and transcribed text of individuals communicating with the human-controlled avatar. A clinical Patient Health Questionnaire (PHQ) score and binary depression decision are provided for each participant. PHQ predictions were obtained by fusing outputs from a Gaussian staircase regressor for each feature set, with results on the development set of mean F1=0.81, RMSE=5.31, and MAE=3.34. These compare favorably to the challenge baseline development results of mean F1=0.73, RMSE=6.62, and MAE=5.52. On test set evaluation, our system obtained a mean F1=0.70, which is similar to the challenge baseline test result. Future work calls for consideration of joint feature analyses across modalities in an effort to detect neurological disorders based on the interplay of motor, linguistic, affective, and cognitive components of communication.
Summary
Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In...
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How deep neural networks can improve emotion recognition on video data
September 25, 2016
Conference Paper
Published in:
ICIP: 2016 IEEE Int. Conf. on Image Processing, 25-28 September 2016.
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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 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.
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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Analytical models and methods for anomaly detection in dynamic, attributed graphs
July 31, 2016
Book Chapter
Published in:
Chapter 2, Computational Network Analysis with R: Applications in Biology, Medicine, and Chemistry, 2017, pp. 35-61.
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Summary
This chapter is devoted to anomaly detection in dynamic, attributed graphs. There has been a great deal of research on anomaly detection in graphs over the last decade, with a variety of methods proposed. This chapter discusses recent methods for anomaly detection in graphs,with a specific focus on detection within backgrounds based on random graph models. This sort of analysis can be applied for a variety of background models, which can incorporate topological dynamics and attributes of vertices and edges. The authors have developed a framework for anomalous subgraph detection in random background models, based on linear algebraic features of a graph. This includes an implementation in R that exploits structure in the random graph model for computationally tractable analysis of residuals. This chapter outlines this framework within the context of analyzing dynamic, attributed graphs. The remainder of this chapter is organized as follows. Section 2.2 defines the notation used within the chapter. Section 2.3 briefly describes a variety of perspectives and techniques for anomaly detection in graph-based data. Section 2.4 provides an overview of models for graph behavior that can be used as backgrounds for anomaly detection. Section 2.5 describes our framework for anomalous subgraph detection via spectral analysis of residuals, after the data are integrated over time. Section 2.6 discusses how the method described in Section 2.5 can be efficiently implemented in R using open source packages. Section 2.7 demonstrates the power of this technique in controlled simulation, considering the effects of both dynamics and attributes on detection performance. Section 2.8 gives a data analysis example within this context, using an evolving citation graph based on a commercially available document database of public scientific literature. Section 2.9 summarizes the chapter and discusses ongoing research in this area.
Summary
This chapter is devoted to anomaly detection in dynamic, attributed graphs. There has been a great deal of research on anomaly detection in graphs over the last decade, with a variety of methods proposed. This chapter discusses recent methods for anomaly detection in graphs,with a specific focus on detection within...
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Feedback-based social media filtering tool for improved situational awareness
May 10, 2016
Conference Paper
Published in:
15th Annual IEEE Int. Symp. on Technologies for Homeland Security, HST 2016, 10-12 May 2016.
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R&D area:
Summary
This paper describes a feature-rich model of data relevance, designed to aid first responder retrieval of useful information from social media sources during disasters or emergencies. The approach is meant to address the failure of traditional keyword-based methods to sufficiently suppress clutter during retrieval. The model iteratively incorporates relevance feedback to update feature space selection and classifier construction across a multimodal set of diverse content characterization techniques. This approach is advantageous because the aspects of the data (or even the modalities of the data) that signify relevance cannot always be anticipated ahead of time. Experiments with both microblog text documents and coupled imagery and text documents demonstrate the effectiveness of this model on sample retrieval tasks, in comparison to more narrowly focused models operating in a priori selected feature spaces. The experiments also show that even relatively low feedback levels (i.e., tens of examples) can lead to a significant performance boost during the interactive retrieval process.
Summary
This paper describes a feature-rich model of data relevance, designed to aid first responder retrieval of useful information from social media sources during disasters or emergencies. The approach is meant to address the failure of traditional keyword-based methods to sufficiently suppress clutter during retrieval. The model iteratively incorporates relevance feedback...
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