&lt;title&gt;Low-power impulse signal classifier using the Haar wavelet transform&lt;/title&gt;

Scholl, James F.; Agre, Jonathan R.; Clare, Loren P.; Gill, M.C.

doi:10.1117/12.336958

Cited by 12 publications

(7 citation statements)

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“…(Note that there are about twice as many wheeled feature vectors as tracked ones.) We note that similar performance has been reported by other researchers in related contexts using wavelet-based processing [18], [19].…”

Section: Low Bandwidth Seismic Datasupporting

confidence: 71%

“…We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques. Tracking multiple targets via a wireless sensor network is a very challenging, multifaceted problem and several research groups have tackled various aspects of it [3]- [8], [12], [13], [15], [18], [19], [21], [23], [25]. We consider the signal processing aspects of this problem under the constraints imposed by limited capabilities of the nodes as well as those associated with networking and routing.…”

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confidence: 99%

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Detection, classification, and tracking of targets

Wong

et al. 2002

IEEE Signal Process. Mag.

741

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Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks becomes a reality. In particular, there are two critical problems underlying successful operation of sensor networks: 1) efficient methods for exchanging information between the nodes and 2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world.This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API) [2]. We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques.

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Section: Low Bandwidth Seismic Datasupporting

confidence: 71%

mentioning

confidence: 99%

Detection, classification, and tracking of targets

Wong

et al. 2002

IEEE Signal Process. Mag.

741

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“…Moreover, the multi-scale feature of the DWT allows the decomposition of an ECG signal into different scales, each of which represents particular coarseness of the signal. Among the various wavelet bases, the Haar wavelet is the shortest and simplest basis and it provides satisfactory localization of signal characteristics in time domain; hence it is ideal for short time signals analysis (Scholl et al, 1999). Therefore, the Haar wavelet was chosen as the mother wavelet in this study.…”

Section: Discrete Wavelet Transformation (Dwt)mentioning

confidence: 99%

Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network

Chen

2007

Pattern Recognition Letters

231

118

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“…Moreover, the multi-scale feature of the DWT allows the decomposition of an ECG signal into different scales. Among various wavelet bases, the Haar wavelet is the shortest and simplest basis that provides satisfactory localization of signal characteristics in time domain [6]. Therefore, the Haar wavelet was chosen as the mother wavelet in this study.…”

Section: A Discrete Wavelet Transformation (Dwt)mentioning

confidence: 99%

Comparison of DifferentWavelet Subband Features in the Classification of ECG Beats Using Probabilistic Neural Network

Chen

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper, an electrocardiogram (ECG) beat classification system based on wavelet transformation and probabilistic neural network (PNN) is proposed to discriminate six ECG beat types. The effects of two wavelet decomposition structures, the two-stage two-band and the two-stage full binary decomposition structures, in the recognition of ECG beat types are studied. The ECG beat signals are first decomposed into components in different subbands using discrete wavelet transformation. Three statistical features of each decomposed subband signals as well as the AC power and instantaneous RR interval of the original signal are exploited to characterize the ECG signals. A PNN then follows to classify the feature vectors. The result shows that features extracted from the decomposed signals based on the two-stage two-band structure outperform the two-stage full binary structure. A promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all type of ECG beats, has been achieved using the optimal feature set. Only 11 features are needed to attain this performance. The results demonstrate the effectiveness and efficiency of the proposed method for the computer-aided diagnosis of heart diseases based on ECG signals.

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<title>Low-power impulse signal classifier using the Haar wavelet transform</title>

Cited by 12 publications

References 0 publications

Detection, classification, and tracking of targets

Detection, classification, and tracking of targets

Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network

Comparison of DifferentWavelet Subband Features in the Classification of ECG Beats Using Probabilistic Neural Network

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