| 11. |
- Axmon, Joakim, et al.
(author)
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A signal model adapted ESPRIT algorithm for joint estimation of spatial and temporal parameters in vibrational analysis of cylinders
- 2002
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In: 2002 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings (Cat. No.02EX593). - 0780375513 ; , s. 360-364
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Conference paper (peer-reviewed)abstract
- This paper deals with the joint estimation of spatial mode shapes and temporal frequencies from transient vibrations measured by a uniform circular array of sensors encircling a cross-section of a cylinder. The geometry allows each 2D mode to be interpreted as two real planar waves impinging from mirrored directions with respect to the broadside of a uniform linear array. Algorithms for joint estimation tend, in the presence of noise, to produce signal roots that express planar waves impinging from only approximately mirrored directions. A recently published 2-D ESPRIT algorithm is modified to take advantage of the geometry, and to force the estimated planar waves to impinge from exactly mirrored directions. This reduces the estimation errors; the main advantage is more easily interpreted results. Memory requirements and complexity are significantly lower for the modified algorithm, since the decomposition of a large data matrix may be broken down into several small ones
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| 12. |
- Axmon, Joakim, et al.
(author)
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Experimental study on the possibility of detecting internal decay in standing Picea abies by blind impact response analysis
- 2004
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In: Forestry. - : Oxford University Press (OUP). - 1464-3626 .- 0015-752X. ; 77:3, s. 179-192
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Journal article (peer-reviewed)abstract
- This paper considers detection of internal decay in standing trees of species Picea abies (L.) Karst. The novel approach is based on two-dimensional spatiotemporal modal analysis of a cross-section which is excited by the hand-made impact of a hammer. An array of accelerometers is distributed around the cross-section, and the resulting impact response is analysed. The temporal frequency for a special spatial mode-shape is used for comparisons on a tree-to-tree basis. The mechanical properties of wood are inherently variable as they are for most materials of biological origin. This leads to a scatter of the analysed parameters that hinders detection of decay based on the temporal frequencies alone. Using regression analysis, we show that by incorporating the additional information on a surface wave propagation velocity, the scatter of sound trees is significantly reduced. The performance of a detector rule which incorporates the frequency and the surface wave propagation velocity is investigated and found to be better than performance reported for visual tree examination. The analyses are based on the impact responses from 94 standing trees, with 66 sound and 28 in various stages of decay. The proposed technique is yet to be considered an experimental tool. Further research, e.g. on how the mechanical properties are influenced by various environmental factors, is needed before the technique can be applied operationally.
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| 13. |
- Axmon, Joakim, et al.
(author)
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Partial Forward-Backward Averaging for Enhanced Frequency Estimation of Real X-texture Modes
- 2005
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In: IEEE Transactions on Signal Processing. - 1053-587X. ; 53:7, s. 2550-2562
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Journal article (peer-reviewed)abstract
- In this paper, enhancement of the signal root estimation of a particular kind of real-valued two-dimensional (2-D) sinusoidal modes is considered. To its constitution, each mode corresponds to the superposition of two real-valued plane waves in a particular symmetry. The concept of partial forward-backward averaging, which is applicable for modes that are undamped in at least one dimension, is introduced as a means for improving the signal subspace estimate from which the signal roots are estimated. The consequences of real-valued signals for the signal root estimates are discussed in detail, and it is shown that by applying partial forward-backward averaging, the mean square errors of the estimates, and the breakdown threshold signal-to-noise ratio (SNR), are significantly reduced, compared with forward-only or conventional forward-backward (when applicable) usage of the sampled signals. The practical implication is highlighted by applying the proposed technique to modal analysis of multichannel impact responses from a tree trunk.
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| 14. |
- Axmon, Joakim, et al.
(author)
-
Partial modal analysis for health assessment of living trees
- 2001
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In: Asia-Pacific conference on non-destructive testing (APCNDT).
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Conference paper (peer-reviewed)abstract
- Rot in living trees cause substantial losses for the forestry industry. The common practice when evaluating forest stands for, e.g., purchase, is assessment based on visual signs. In this paper a new non-destructive assessment method based on the impact excitation method is proposed. The trunk of a living tree is excited by the impact of a hammer, and the vibrations are measured by accelerometers. Resonance frequencies, circumferential mode shapes and propagation velocity of a surface wave are analysed. A function describing the expected frequency for a sound tree is derived, and used in a detector whose performance is evaluated for 93 trees of species Norway spruce. The partial mode shape is used to ensure that the corresponding resonance frequencies are compared to each other. It is found that the detector is successful and outperforms assessments by skilled experts in forestry.
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| 15. |
- Bailon, R, et al.
(author)
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A robust method for ECG-Based estimation of the respiratory frequency during stress testing
- 2006
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In: IEEE Transactions on Biomedical Engineering. - 1558-2531. ; 53:7, s. 1273-1285
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Journal article (peer-reviewed)abstract
- A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the heart's electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% +/- 0.2%, mean SD (0.002 +/- 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9 % +/- 4 % (0.022 +/- 0.016 Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.
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| 16. |
- Bailon, Raquel, et al.
(author)
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Analysis of heart rate variability during exercise stress testing using respiratory information
- 2010
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In: Biomedical Signal Processing and Control. - : Elsevier BV. - 1746-8094. ; 5:4, s. 299-310
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Journal article (peer-reviewed)abstract
- This paper presents a novel method for the analysis of heart rate variability (HRV) during exercise stress testing enhanced with respiratory information. The instantaneous frequency and power of the low frequency (LF) and high frequency (HF) bands of the HRV are estimated by parametric decomposition of the instantaneous autocorrelation function (ACF) as a sum of damped sinusoids. The instantaneous ACF is first windowed and filtered to reduce the cross terms. The inclusion of respiratory information is proposed at different stages of the analysis, namely, the design of the filter applied to the instantaneous ACF, the parametric decomposition, and the definition of a dynamic HF band. The performance of the method is evaluated on simulated data as well as on a stress testing database. The simulation results show that the inclusion of respiratory information reduces the estimation error of the amplitude of the HF component from 3.5% to 2.4% in mean and related SD from 3.0% to 1.7% when a tuned time smoothing window is used at an SNR of 15 dB. Results from the stress testing database show that information on respiratory frequency produces HF power estimates which closely resemble those from the simulations which exhibited lower SD. The mean SD of these estimates with respect to their mean trends is reduced by 84% (from 0.74 x 10(-3) s(-2) to 0.12 x 10(-3) s(-2)). The analysis of HRV in the stress testing database reveals a significant decrease in the power of both the LF and HF components around peak stress. (C) 2010 Elsevier Ltd. All rights reserved.
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| 17. |
- Bailon, R., et al.
(author)
-
Analysis of heart rate variability using time-varying frequency bands based on the respiratory frequency
- 2007
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In: Proc. IEEE Conf. Eng. Med. Biol.
-
Conference paper (peer-reviewed)abstract
- In this paper a methodological approach for the analysis of nonstationary heart rate variability (HRV) signals using time-varying frequency bands based on respiratory frequency is presented. Spectral analysis of HRV is accomplished by means of the Smoothed Pseudo Wigner Ville distribution. Different approaches to the definition of the low frequency (LF) and high frequency (HF) bands are considered which involve respiratory information, derived either from a respiratory signal or from the ECG itself. Results are presented which derive from recordings acquired during stress testing and induced emotion experiments.
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| 18. |
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| 19. |
- Behjat, Hamid, et al.
(author)
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Anatomically-adapted Graph Wavelets for Improved Group-level fMRI Activation Mapping
- 2015
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In: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 123:Online 07 June 2015, s. 185-199
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Journal article (peer-reviewed)abstract
- A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar grey matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.
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| 20. |
- Behjat, Hamid, et al.
(author)
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Canonical cerebellar graph wavelets and their application to fMRI activation mapping
- 2014
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In: [Host publication title missing]. - 1557-170X. ; , s. 1039-1042
-
Conference paper (peer-reviewed)abstract
- Wavelet-based statistical parametric mapping (WSPM) is an extension of the classical approach in fMRI activation mapping that combines wavelet processing with voxel-wise statistical testing. We recently showed how WSPM, using graph wavelets tailored to the full gray-matter (GM) structure of each individual’s brain, can improve brain activity detection compared to using the classical wavelets that are only suited for the Euclidian grid. However, in order to perform analysis on a subject-invariant graph, canonical graph wavelets should be designed in normalized brain space. We here introduce an approach to define a fixed template graph of the cerebellum, an essential component of the brain, using the SUIT cerebellar template. We construct a corresponding set of canonical cerebellar graph wavelets, and adopt them in the analysis of both synthetic and real data. Compared to classical SPM, WSPM using cerebellar graph wavelets shows superior type-I error control, an empirical higher sensitivity on real data, as well as the potential to capture subtle patterns of cerebellar activity.
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