Compact optical instrument for surface classification using self-mixing interference in a laser diode

Özdemir, Şahin Kaya; Shinohara, S.; Ito, Satoshi; Yoshida, H.

doi:10.1117/1.1331268

Cited by 30 publications

(16 citation statements)

References 14 publications

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“…The simulation results corresponding to the same weight values are plotted in dashed. Second multiplier transfer approximation as a function of hyperbolic tangent output voltage is (10) Thus, neuron transfer equation is (11) Curves relative to (10) and (11) have been represented with dots in Fig. 14.…”

Section: Second Multiplier-global Neuronmentioning

confidence: 99%

“…NN have become an effective solution to improve the robustness and the data processing of integrated sensors [13]- [16]. Multilayer perceptrons (MLPs) are notably used in the case of continuous data values classification, such as surface detection [10]. However, there is no system or procedure capable of discriminating a wide range of surface types.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the type of application, different types of sensors may be used to detect surfaces: optical (in visible wavelengths [1], [10] or in infrared [8], [11]), ultrasonic [12], radar-based, and so on. One well-known way to improve surfaces discrimination is to use artificial neural networks (NNs) [7], [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Analog Neural Network Implementation for a Real-Time Surface Classification Application

2008

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This paper deals with the implementation of a CMOS analog neural network (NN) that has to be integrated in a new kind of optoelectronic measurement system. The aim is to achieve real-time surface recognition using a phase-shift rangefinder and a neural network. NN architecture is a multilayer perceptron (MLP) with two analog input signals provided by the rangefinder, three processing neurons in the hidden layer, and one output neuron whose output voltage indicates the detected surface. As the complete structure is analog, no analog-to-digital conversions or signal processing between the rangefinder and the network is necessary. Furthermore, the 3.3-V voltage supply, relative to the chosen CMOS 0.35-m technology, allows to reduce the system power consumption. This paper focuses on the implementation in an ASIC of an elementary part of the NN, called neuron, and on the achievement of the complete NN from the integration of three ASICs in a printed circuit board. Comparisons between ideal case, simulations and tests are detailed in order to validate the design and the good functioning of the complete structure.Index Terms-Analog multiplier, analog processing circuits, CMOS analog integrated circuits, laser applications, multilayer perceptron, neural network applications, neural network hardware.

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Section: Second Multiplier-global Neuronmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analog Neural Network Implementation for a Real-Time Surface Classification Application

2008

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“…Starting from mid-1980's, there has been a growing interest in SM-LD based sensors for ranging, velocimetry, particlesizing, displacement and vibrometry among others [10], [11], [12], [13], [14]. Inspired by the success of such measurements on solid objects, the use of Doppler effect in SM-LD schemes has been proposed and applied for blood flow measurement [15], [16].…”

Section: Introductionmentioning

confidence: 98%

Assessment on Self-mixing Laser Interferometry for Blood flow Measurement over Skin Surface

Ozdemir

Ohno

Shinohara

2006

2006 IEEE Instrumentation and Measurement Technology Conference Proceedings

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We study self-mixing laser diode (SM-LD) as a low-cost and compact optical sensor for non-invasive blood flow measurement and profiling over the skin surface. By making in-vivo and in-vitro measurements, we compare the SM-LD based sensor with the commercially available flowmeters to assess the accuracy and feasibility of SM-LD sensors for such applications. For the SM-LD based sensor, we apply two different signal processing methods (i) counting the intensity fluctuations of the signal from the SM-LD to obtain a frequency value, and (ii) the autocorrelation of the signal. In-vitro measurements show good agreement with the commercially available flowmeters. In-vivo measurements performed on test subjects revealed that autocorrelation technique shows much better results than the counting technique in blood flow profiling and SM-LD based sensor suffers from errors caused by external artifacts during measurements.

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“…Same behavior shall be observed when the LD is tilted from an initial position by a small angle ( Fig.1(C)). However, as explained in [6] due to the roughness of the surface, "the scattered fields from every point illuminated by the light beam add with markedly different phases, resulting in an interference pattern known as speckles".…”

Section: Introductionmentioning

confidence: 99%

Optical feedback interferometry for raster scan profilometry

et al. 2017

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Compact optical instrument for surface classification using self-mixing interference in a laser diode

Cited by 30 publications

References 14 publications

Analog Neural Network Implementation for a Real-Time Surface Classification Application

Analog Neural Network Implementation for a Real-Time Surface Classification Application

Assessment on Self-mixing Laser Interferometry for Blood flow Measurement over Skin Surface

Optical feedback interferometry for raster scan profilometry

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