Robust discrimination of human footsteps using seismic signals

Faghfouri, A.; Frish, Michael B.

doi:10.1117/12.882726

Cited by 15 publications

(4 citation statements)

References 7 publications

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“…In preparing class signatures, initially we tried to fuzzy classify all detected signals, however we were not able to map input signals to fuzzy input and output variables as in [75]. Thereafter, we have made use of acoustic and seismic data properties for the initial level classification as described in [76] and [77], respectively. For the acoustic detection, acoustic signal is analyzed to determine the presence of a target by using Binary Fuzzy Classification (BFC) as described in [78].…”

Section: Hierarchical Data Fusionmentioning

confidence: 99%

“…For the seismic detection, initially detected signal is segmented into 0.75 second windows and for each window a set of features which forms the feature-vector are extracted. A total number of 60 features are defined by using spectral technique (Fourier transform), statistical techniques (mean and variance) and entropy of the signal as described in [77]. If the initial level classification result is human/vehicle, then a trigger is sent to the camera sensor on the node to activate it; if not we simply discard the result.…”

Section: Hierarchical Data Fusionmentioning

confidence: 99%

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An efficient fuzzy fusion-based framework for surveillance applications in Wireless Multimedia Sensor Networks

Sert

Yazıcı

Çoşar

et al. 2014

2014 International Wireless Communications and Mobile Computing Conference (IWCMC)

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, 86 pages Previous advances in Information Technologies and especially in Micro Electro-Mechanical Systems, have made the production and deployment of tiny, battery-powered nodes communicating over wireless links possible. Networks comprised of such nodes with sensing capability are called Wireless Sensor Networks. The early deployment aim was to use these nodes only in a passive way for indoor applications. These kinds of early nodes had the ability to sense scalar data such as temperature, humidity, pressure and location of surrounding objects. However, recently available sensor nodes have higher computation capability, higher storage space and better power solutions with respect to their predecessors. With these developments in addition to scalar data delivery, multimedia content has become the core focus. A wireless sensor network with multimedia capabilities is called Wireless Multimedia Sensor Network. There has always been a trade-off between accuracy and energy-efficiency in these new generation networks because of their resource-constrained nature. In this thesis we introduce a new approach to address this trade-off in Wireless Multimedia Sensor Networks. Although a number of previous studies have focused on various special topics in Wireless Multimedia Sensor Networks in detail, to the best of our knowledge, none presents a fuzzy multi-modal data fusion system, which is lightweight and provides a high accuracy ratio. Especially, a multi-modal data fuv sion system targeting surveillance applications make it inevitable to work within a multi-level hierarchical framework. In this thesis, our primary focus is on accuracy and efficiency by utilizing our framework. Along with the fuzzy fusion framework, a new fuzzy clustering algorithm, namely Multi-Objective Fuzzy Clustering Algorithm (MOFCA), is introduced and evaluated in detail as well.

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Section: Hierarchical Data Fusionmentioning

confidence: 99%

Section: Hierarchical Data Fusionmentioning

confidence: 99%

An efficient fuzzy fusion-based framework for surveillance applications in Wireless Multimedia Sensor Networks

Sert

Yazıcı

Çoşar

et al. 2014

2014 International Wireless Communications and Mobile Computing Conference (IWCMC)

View full text Add to dashboard Cite

show abstract

“…Sensor networks are common to use for human/animal classification [2], human footstep discrimination [3], condition monitoring in the railway industry [4], vehicle detection and classification [5,6], urban traffic management [7], vehicle speed estimation [8], supporting environments for multimedia surveillance [9], or discriminating humans, animals, and vehicles [10]. Tracked and trackless vehicle detection and classification with distributed sensor networks as a counter camouflage technique is also one of the popular application areas [11].…”

Section: Introductionmentioning

confidence: 99%

A new spectral estimation-based feature extraction method for vehicle classification in distributed sensor networks

Köse¹,

Hocaoglu²

2019

Turk J Elec Eng & Comp Sci

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Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic sensors and showed that the classification performance varies with the type of road due to the distinct spectral characteristics of the medium. Our proposed classifier fuses time and frequency-domain features and acoustic and seismic modalities to achieve the highest classification rate of 98.6% using a relatively small number of features.

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“…He [7] proposed a novel vibration-based fault diagnosis algorithm for a machine condition monitoring system using the wavelet packet transform (WPT) features. In addition to these studies, fruitful achievements on vibration processing can be found in [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Vibration source classification and propagation distance estimation system based on spectrogram and KELM

Chen

Cao

Lin³

et al. 2019

Cognitive Computation and Systems

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Earth surface vibration signals source classification and propagation distance estimation attract increasing attention in recent years due to the wide applications in many areas. In this study, the authors develop a hybrid classification and propagation distance estimation algorithm for general earth surface vibration sources. The spectrogram (SPEC) feature characterising the energy distribution of vibrations is first developed for signal representation in this study. The kernel-based extreme learning machine (KELM) algorithm is then adopted for the vibration source classification and propagation distance estimation. Comparing with the conventional approaches, the proposed KELM + SPEC algorithm is not only effective in characterising the time-and frequency-domain features of vibrations, but also superior in accuracy and efficiency. To test the effectiveness of the proposed KELM + SPEC algorithm, experiments on real collected vibration signals are presented, where simulations on both periodic and aperiodic vibrations are carried out in the study. Comparisons to various existing vibration signal extraction and classification algorithms are provided to show the advantages of the proposed KELM + SPEC algorithm.

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Robust discrimination of human footsteps using seismic signals

Cited by 15 publications

References 7 publications

An efficient fuzzy fusion-based framework for surveillance applications in Wireless Multimedia Sensor Networks

An efficient fuzzy fusion-based framework for surveillance applications in Wireless Multimedia Sensor Networks

A new spectral estimation-based feature extraction method for vehicle classification in distributed sensor networks

Vibration source classification and propagation distance estimation system based on spectrogram and KELM

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