In memoriam: Hendrik W. Bode (1905-1982)

Valkenburg, M. Van

doi:10.1109/tac.1984.1103509

Cited by 11 publications

(3 citation statements)

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“…A circuit simulation tool, one of the popular SPICE applications [18][19] was utilized to design the filter. The characterization of amplitude and corresponding phase of the ANC circuit was visualized graphically in a bode plot [20]. Figure 8 shows the plot of the measured Microphone and Loudspeaker behavior, recorded by a network analyzer by swiping all frequencies on the input and measuring the output response.…”

Section: Resultsmentioning

confidence: 99%

Double Skin Facades with Natural Ventilation Capability: A Case Study of Acoustical Enhancement via Passive and Active (Noise Cancelling) Methods

Tauber

Bajraktari

Lechleitner

et al. 2016

AMM

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Within an ongoing project, we explore the potential of double skin facades to provide both noise control and natural ventilation capability. Three strategies are investigated: i) Manipulation of sound paths via offset of the openings in the two shells of the façade; ii) Application of absorbing materials in the interstitial space of the façade; iii) Active noise cancelling methods utilizing wave-destructive interference. This contribution describes the overall project but focuses primarily on the active noise cancellation approach. Aside from a comprehensive background research on existing technology, we undertook the design of an actual setting for noise cancellation testing with suitable low-cost components as a proof-of-concept. Results of the experiments are expected to inform subsequent efforts to include noise-cancelling technology in double skin facades.

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Section: Resultsmentioning

confidence: 99%

Double Skin Facades with Natural Ventilation Capability: A Case Study of Acoustical Enhancement via Passive and Active (Noise Cancelling) Methods

Tauber

Bajraktari

Lechleitner

et al. 2016

AMM

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“…As a consequence, the proposed extension is fundamental both from a Machine Learning as well as from a Chemometrics perspective [23]. Because the above relations are also analogous to the blurring function (relating amplitude and phase) developed by Bode [24] to describe the dynamics of physical systems, such extension has a wide range of applications across all physical sciences. In this context, this study focuses on the use of CELM to perform binary and multi-class classification of RNA and powder samples respectively on the basis of images acquired by a THz-transient imaging spectrometer.…”

Section: Classification Methodologymentioning

confidence: 99%

Complex extreme learning machine applications in terahertz pulsed signals feature sets

Yin

Hadjiloucas

Zhang

2014

Computer Methods and Programs in Biomedicine

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This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

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“…It is thus fundamental both from a machine learning as well as from a chemometrics perspective [143]. Because spectroscopic responses are analogous to the blurring function (relating amplitude and phase) developed by Bode [144] to describe the dynamics of physical systems, CELMs have a wide range of applications across all physical sciences. A typical example of the proposed approach is the recent study [145] which focused on the use of CELM to perform binary and multi-class classification of RNA and powder samples respectively on the basis of images acquired by THz-TPI.…”

Section: Analysis and Classification Of Thz Imaging Datamentioning

confidence: 99%

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

Yin

Hadjiloucas

Zhang

et al. 2016

Artificial Intelligence in Medicine

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We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods: Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation: Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results: Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion: The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.

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In memoriam: Hendrik W. Bode (1905-1982)

Cited by 11 publications

References 0 publications

Double Skin Facades with Natural Ventilation Capability: A Case Study of Acoustical Enhancement via Passive and Active (Noise Cancelling) Methods

Double Skin Facades with Natural Ventilation Capability: A Case Study of Acoustical Enhancement via Passive and Active (Noise Cancelling) Methods

Complex extreme learning machine applications in terahertz pulsed signals feature sets

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

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