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

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

doi:doi:10.3788/COL202018.011201

Chinese Optics Letters, 2020, 18 (1): 011201, Published Online: Dec. 10, 2019

Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor Download： 639次

Muhammad Usman ¹Usman Zabit ^2,*Olivier D. Bernal ³Gulistan Raja ⁴

Author Affiliations

¹ Namal Institute, Mianwali 42250, Pakistan

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

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

⁴ University of Engineering & Technology, Taxila 47080, Pakistan

Figures & Tables

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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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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Table1. 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

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

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Table2. Values of Statistical Parameters of Simulated Normalized Mono-Modal SM Signals for Varying SNRs under Moderate-Feedback Regime (C=4) and Amplitude of 5λ0

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

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Table3. 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

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

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Table4. Performance of Proposed Techniques by Testing Experimentally Acquired Dataset of 60 SM Signals

Techs.	$N_{mon} / N_{mul}$	$N_{mon-Ti}$	$N_{mul-Ti}$	$N_{mon-Fi}$	$N_{mul-Fi}$	$N_{to-Ti}$	$R_{s} / %$
VBT	30/30	30	21	9	0	51	85
KBT	30/30	24	21	9	6	45	75
SBT	30/30	29	26	4	1	55	91
SKBT	30/30	26	30	0	4	56	93
MV	30/30	30	30	0	0	100	100

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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.

Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor Download： 639次

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.

Fig. 2. Flowchart of customized maxima detection technique variations ( ${var}_{p-diff}$ ).

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

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

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) $| {SM}_{skur} |$ .

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 ( $C = 4$ ).

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

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

Table1. 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

Table2. Values of Statistical Parameters of Simulated Normalized Mono-Modal SM Signals for Varying SNRs under Moderate-Feedback Regime (C=4) and Amplitude of 5λ0

Table3. 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

Table4. Performance of Proposed Techniques by Testing Experimentally Acquired Dataset of 60 SM Signals

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Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor Download： 639次

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 (Ith=45 mA) with Ioper of (a), (c) 78 mA and (b) 82 mA.

Fig. 2. Flowchart of customized maxima detection technique variations (varp-diff).

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

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

Fig. 5. Evolution of parameters with respect to C and target vibration amplitude for noiseless mono-modal signal (a) varp-diff, (b) SMkur, (c) SMskw, and (d) |SMskur|.

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).

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

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

Table1. 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

Table2. Values of Statistical Parameters of Simulated Normalized Mono-Modal SM Signals for Varying SNRs under Moderate-Feedback Regime (C=4) and Amplitude of 5λ0

Table3. 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

Table4. Performance of Proposed Techniques by Testing Experimentally Acquired Dataset of 60 SM Signals

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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.

Fig. 2. Flowchart of customized maxima detection technique variations ( ${var}_{p-diff}$ ).

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) $| {SM}_{skur} |$ .

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 ( $C = 4$ ).