An accumulative fusion architecture for discriminating people and vehicles using acoustic and seismic signals

Lee, Kyunghun; Riggan, Benjamin S.; Bhattacharyya, Shuvra S.

doi:10.1109/icassp.2017.7952702

Cited by 12 publications

(17 citation statements)

References 12 publications

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“…In this work, we build on our recent algorithmic investigation of PVD using acoustic and seismic signals [6]. In this investigation, we introduced an adaptation to PVD of sensor fusion based on Dempster-Shafer Theory (DST) [7], [8], and we also introduced a PVD algorithm, called Accumulation of Local Feature-level Fusion Scores (ALFFS).…”

Section: Related Workmentioning

confidence: 99%

“…ALFFS extracts cepstral features, and applies an accumulative, feature-level fusion approach. Our work in this paper employs the algorithms developed in [6] as a starting point, and addresses system design and implementation challenges that are critical to practical deployment of the algorithms. The system design and implementation-oriented focus of this work is significantly different from that of [6], which is focused on algorithmic aspects.…”

Section: Related Workmentioning

confidence: 99%

“…The presentation here is focused on highlighting relevant aspects of the embedded software architecture. For details of the underlying algorithms, we refer the reader to [6].…”

Section: System Designmentioning

confidence: 99%

“…The µ alf mode is the only PVD system mode that requires thr = cns, due to the windowed behavior of the underlying ALFFS algorithm [6].…”

Section: Alffs Modementioning

confidence: 99%

“…This actor applies a dual-modality fusion algorithm based on Dempster-Shafer Theory, as mentioned in Section II. For complete details on this algorithm, we refer the reader to [6]. In a given firing, the DST Fusion actor takes as input two frames of cepstral coefficients a(1), a(2), .…”

Section: B Dst-based Fusion Modementioning

confidence: 99%

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An optimized embedded target detection system using acoustic and seismic sensors

Lee

Riggan

Bhattacharyya

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Detection of targets using low power embedded devices has important applications in border security and surveillance. In this paper, we build on recent algorithmic advances in sensor fusion, and present the design and implementation of a novel, multi-mode embedded signal processing system for detection of people and vehicles using acoustic and seismic sensors. Here, by "multi-mode", we mean that the system has available a complementary set of configurations that are optimized for different trade-offs. The multimode capability delivered by the proposed system is useful to supporting long lifetime (long term, energy-efficient "standby" operation), while also supporting optimized accuracy during critical time periods (e.g., when a potential threat is detected). In our target detection system, we apply a strategically-configured suite of single-and dual-modality signal processing techniques together with dataflow-based design optimization for energyefficient, real-time implementation. Through experiments using a Raspberry Pi platform, we demonstrate the capability of our target detection system to provide efficient operational tradeoffs among detection accuracy, energy efficiency, and processing speed.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The presentation here is focused on highlighting relevant aspects of the embedded software architecture. For details of the underlying algorithms, we refer the reader to [6].…”

Section: System Designmentioning

confidence: 99%

“…The µ alf mode is the only PVD system mode that requires thr = cns, due to the windowed behavior of the underlying ALFFS algorithm [6].…”

Section: Alffs Modementioning

confidence: 99%

Section: B Dst-based Fusion Modementioning

confidence: 99%

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An optimized embedded target detection system using acoustic and seismic sensors

Lee

Riggan

Bhattacharyya

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Software synthesis from dataflow schedule graphs

Lee

Raina

et al. 2021

SN Appl. Sci.

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The dataflow-model of computation is widely used in design and implementation of signal processing systems. In dataflow-based design processes, scheduling—the assignment and coordination of computational modules across processing resources—is a critical task that affects practical measures of performance, including latency, throughput, energy consumption, and memory requirements. Dataflow schedule graphs (DSGs) provide a formal abstraction for representing schedules in dataflow-based design processes. The DSG abstraction allows designers to model a schedule as a separate dataflow graph, thereby providing a formal, abstract (platform- and language-independent) representation for the schedule. In this paper, we introduce a design methodology that is based on explicit specifications of application graphs and schedules as cooperating dataflow models. We also develop new techniques and tools for automatically synthesizing efficient implementations on multicore platforms from these coupled application and schedule models. We demonstrate the proposed methodology and synthesis techniques through a case study involving real-time detection of people and vehicles using acoustic and seismic sensors.

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Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks

Roheda

Riggan

Krim

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i.e. transferring) knowledge from sensor data and enhancing low-resolution target detection. In unconstrained surveillance settings, sensor measurements are often noisy, degraded, corrupted, and even missing/absent, thereby presenting a significant problem for multi-modal fusion. We therefore specifically tackle the problem of a missing modality in our attempt to propose an algorithm based on CGANs to generate representative information from the missing modalities when given some other available modalities. Despite modality gaps, we show that one can distill knowledge from one set of modalities to another. Moreover, we demonstrate that it achieves better performance than traditional approaches and recent teacher-student models.

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An accumulative fusion architecture for discriminating people and vehicles using acoustic and seismic signals

Cited by 12 publications

References 12 publications

An optimized embedded target detection system using acoustic and seismic sensors

An optimized embedded target detection system using acoustic and seismic sensors

Software synthesis from dataflow schedule graphs

Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks

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