The T-class of time–frequency distributions: Time-only kernels with amplitude estimation

Hussain, Zahir M.; Boashash, Boualem

doi:10.1016/j.jfranklin.2006.03.020

Cited by 18 publications

(4 citation statements)

References 17 publications

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“…In [ 11 ], the authors presented an approach for estimating the frequency of linear frequency modulation (LFM) signals, a topic that has applications in radar and communication engineering. The approach utilized a convolutional neural network (CNN), while this problem was traditionally handled using time–frequency analysis [ 12 ]; however, this approach requires significant computational cost as it involves two-dimensional transforms.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Almayyali

Hussain

2021

Sensors

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Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.

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Section: Motivation and Related Workmentioning

confidence: 99%

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Almayyali

Hussain

2021

Sensors

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“…The ICI (intersection of confidence intervals)-based methods utilize a nonparametric IF estimation approach to make a trade-off between bias and variance of the estimated IF [2,28,29]. Also, IF estimation using the properties of TFDs including adaptive short-time Fourier transform [31], QTFDs [32], T-class of TFDs [33], polynomial WVDs (PWVDs) [34,35] and complex-time distributions (CTDs) [36,37] have received a lot of attention. A detailed review on IF estimation algorithms can be found in [25][26][27].…”

Section: Instantaneous Frequency: Definition and Estimationmentioning

confidence: 99%

A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

Omidvarnia

Azemi

Colditz

et al. 2013

Digital Signal Processing

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This paper proposes a new approach to estimate the phase synchrony among nonstationary multivariate signals using the linear relationships between their instantaneous frequency (IF) laws. For cases where nonstationary signals are multi-component, a decomposition method like multi-channel empirical mode decomposition (MEMD) is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). We then apply the Johansen method on the IF laws to assess the phase synchrony within multivariate nonstationary signals. The proposed approach is validated first using multichannel synthetic signals. The method is then used for quantifying the inter-hemispheric EEG asynchrony during ictal and inter-ictal periods using a newborn EEG seizure/non-seizure database of five subjects. For this application, pair-wise phase synchrony measures may not be able to account for phase interactions between multiple channels. Furthermore, the classical definition of phase synchrony, which is based on the rational relationships between phases, may not reveal the hidden phase interdependencies caused by irrational long-run relationships. We evaluate the performance of the proposed method using the differentiation of unwrapped phase as well as other IF estimation techniques. The results obtained on newborn EEG signals confirm that the generalized phase synchrony within EEG channels increases significantly during ictal periods. A statistically consistent phase coupling is also observed within the non-seizure segments supporting the concept of constant inter-hemispheric connectivity in the newborn brain during inter-ictal periods.

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“…However, using a low-pass time-only kernel other than δ(t) will result in controlling the cross-terms by the low-pass function g [21].…”

Section: The Wigner-ville Distributionmentioning

confidence: 99%

“…The controlling parameter σ = 0.05 for HTD, 0.015 for ETD, and 19 for CWD. These are practically the optimal values for these TFDs that balance between resolution and cross terms reduction [21]. Any change will compromise one of these factor against the other.…”

Section: Surface Electromyogram With Ecg Artifactsmentioning

confidence: 99%

Time-frequency analysis of normal and abnormal biological signals

Mahmoud

Hussain

Cosic

et al. 2006

Biomedical Signal Processing and Control

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The T-class of time–frequency distributions: Time-only kernels with amplitude estimation

Cited by 18 publications

References 17 publications

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

Time-frequency analysis of normal and abnormal biological signals

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