8,623 research outputs found
SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization
Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.Comment: 12 pages, 9 figure
Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition
Real-time defect detection is crucial in laser-directed energy deposition
(L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach
utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture
the complex process dynamic behaviors, which is insufficient for defect
detection with high accuracy and robustness. This paper proposes a novel
multimodal sensor fusion method for real-time location-dependent defect
detection in the robotic L-DED process. The multimodal fusion sources include a
microphone sensor capturing the laser-material interaction sound and a visible
spectrum CCD camera capturing the coaxial melt pool images. A hybrid
convolutional neural network (CNN) is proposed to fuse acoustic and visual
data. The key novelty in this study is that the traditional manual feature
extraction procedures are no longer required, and the raw melt pool images and
acoustic signals are fused directly by the hybrid CNN model, which achieved the
highest defect prediction accuracy (98.5 %) without the thermal sensing
modality. Moreover, unlike previous region-based quality prediction, the
proposed hybrid CNN can detect the onset of defect occurrences. The defect
prediction outcomes are synchronized and registered with in-situ acquired robot
tool-center-point (TCP) data, which enables localized defect identification.
The proposed multimodal sensor fusion method offers a robust solution for
in-situ defect detection.Comment: 8 pages, 10 figures. This paper has been accepted to be published in
the proceedings of IDETC-CIE 202
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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