Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor

Muhammad Usman; Usman Zabit; Olivier D. Bernal; Gulistan Raja

doi:10.3788/COL202018.011201

Journals >Chinese Optics Letters >Volume 18 >Issue 1 >Page 011201 > Article

Chinese Optics Letters
Vol. 18, Issue 1, 011201 (2020)

Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor

Muhammad Usman¹, Usman Zabit^2、*, Olivier D. Bernal³, and Gulistan Raja⁴

Author Affiliations

¹Namal Institute, Mianwali 42250, Pakistan

²National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan

³Univ de Toulouse, INP, LAAS-CNRS, Toulouse F-31400, France

⁴University of Engineering & Technology, Taxila 47080, Pakistan

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DOI: 10.3788/COL202018.011201 Cite this Article Set citation alerts

Muhammad Usman, Usman Zabit, Olivier D. Bernal, Gulistan Raja. Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor[J]. Chinese Optics Letters, 2020, 18(1): 011201 Copy Citation Text

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Experimental (a), (b) multi- and (c) mono-modal SM signals acquired under different OF coupling and operating current conditions based on the HL6501MG LD (Ith=45 mA) with Ioper of (a), (c) 78 mA and (b) 82 mA.

Fig. 1. Experimental (a), (b) multi- and (c) mono-modal SM signals acquired under different OF coupling and operating current conditions based on the HL6501MG LD (

I_{th} = 45 mA

) with

I_{oper}

of (a), (c) 78 mA and (b) 82 mA.

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Flowchart of customized maxima detection technique variations (varp-diff).

Fig. 2. Flowchart of customized maxima detection technique variations (

{var}_{p-diff}

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Fig. 3. Flowchart of the variance-based technique (VBT).

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Fig. 4. Steps of the kurtosis-based technique (KBT), skewness-based technique (SBT), and skewness–kurtosis-based technique (SKBT).

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Fig. 5. Evolution of parameters with respect to

C

and target vibration amplitude for noiseless mono-modal signal (a)

{var}_{p-diff}

, (b)

{SM}_{kur}

, (c)

{SM}_{skw}

, and (d)

| {SM}_{skur} |

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Fig. 6. Simulated mono-modal SM signals with SNRs of 10 dB and 40 dB in the case of (a) weak feedback regime (

C = 0.1

) and (b) moderate feedback regime (

C = 4

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Fig. 7. Correctly identified experimental mono-modal SM signals.

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Fig. 8. Correctly identified experimental (a) bi-modal and (b) tri-modal SM signals.

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Techs.	Feats.	SNR (10 dB)	SNR (20 dB)	SNR (30 dB)	SNR (40 dB)
VBT	${var}_{p ‐ diff}$	0.015	0.012	0.012	0.008
KBT	${SM}_{kur}$	1.718	1.452	1.433	1.429
SBT	${SM}_{skw}$	0.149	0.144	0.142	0.141
SKBT	$\| {SM}_{skur} \|$	11.458	10.053	9.821	9.510

Table 1. Values of Statistical Parameters of Simulated Normalized Mono-Modal SM Signals for Varying SNR under Weak-Feedback Regime for C=0.1 and Amplitude of 5λ0

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Techs.	Feats.	SNR (10 dB)	SNR (20 dB)	SNR (30 dB)	SNR (40 dB)
VBT	${var}_{p ‐ diff}$	0.016	0.011	0.007	0.006
KBT	${SM}_{kur}$	2.267	2.253	2.099	2.007
SBT	${SM}_{skw}$	−0.345	−0.340	−0.326	−0.320
SKBT	$\| {SM}_{skur} \|$	6.777	6.627	6.162	6.103

Table 2. Values of Statistical Parameters of Simulated Normalized Mono-Modal SM Signals for Varying SNRs under Moderate-Feedback Regime (C=4) and Amplitude of 5λ0

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	VBT	KBT	SBT	SKBT
Features	${var}_{p ‐ diff}$	${SM}_{kur}$	${SM}_{skw}$	$\| {SM}_{skur} \|$
Threshold values	0.017	2.7	0.2	5

Table 3. Extracted Threshold Values of Proposed Statistical Parameters Based on Simulations on Mono-Modal SM Signals under Varying Optical Feedback, Vibration Amplitude, and Signal to Noise Ratio

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