Analysis Of Speckle Patterns Research Articles

Microsaccades Tracking by Secondary Speckle Pattern Analysis.

Here we propose a not pupil-dependent microsaccades tracking technique and a novel detection method. We present a proof of concept for detecting microsaccades using a non-contact laser-based photonic system recording and processing the temporal changes of speckle patterns scattered from an eye sclera. The data, simultaneously recorded by the speckle-based tracker (SBT) and the video-based eye tracker (Eyelink), was analyzed by the frequently used detection method of Engbert and Kliegl (E&K) and by advanced machine learning detection (MLD) techniques. We detected 93% of microsaccades in the SBT data out of microsaccades detected in the Eyelink data with the E&K method. By utilizing MLD, a precision of 86% was achieved. The findings of our study demonstrate a potential improvement in measuring tiny eye movements, such as microsaccades, using speckle-based eye tracking and, thus, an alternative to video-based eye tracking for detecting microsaccades.

Speckle Pattern Analysis of PVK:rGO Composite Based Memristor Device

AbstractThe memristors are expected to be fundamental devices for neuromorphic systems and switching applications. The device made of a sandwiched layer of poly(N‐ vinylcarbazole) and reduced graphene composite between asymmetric electrodes (ITO/PVK:rGO/Al) exhibits bistable resistive switching behavior. The performance of the memristor can be optimized by controlling the doped graphene oxide. To assess the device performance when it switches between ON and OFF states, optical characterization approaches are highly promising due to their non‐destructive and remote nature. Here, speckle pattern (SP) analysis to this end is introduced. SPs include a huge amount of information about their generating mechanism, which is extracted through statistical elaboration. SPs of the PVK:rGO in different states in situ and examine the conduction mechanism is acquired. The variations in the statistical parameters are attributed to the resistance state of the PVK:rGO with regard to the physical switching mechanism. The resistance/conduction state, in turn, depends on the activity and properties of PVK:rGO memristors, as well as the additional non‐uniformities induced through the variations of density of carriers. The present optical methodology can be potentially served as a bench‐top device for characterization purposes of similar devices during their operating.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns.

Glaucoma, a leading cause of global blindness, disproportionately affects low-income regions due to expensive diagnostic methods. Affordable intraocular pressure (IOP) measurement is crucial for early detection, especially in low- and middle-income countries. We developed a remote photonic IOP biomonitoring method by deep learning of the speckle patterns reflected from an eye sclera stimulated by a sound source. We aimed to achieve precise IOP measurements. IOP was artificially raised in 24 pig eyeballs, considered similar to human eyes, to apply our biomonitoring method. By deep learning of the speckle pattern videos, we analyzed the data for accurate IOP determination. Our method demonstrated the possibility of high-precision IOP measurements. Deep learning effectively analyzed the speckle patterns, enabling accurate IOP determination, with the potential for global use. The novel, affordable, and accurate remote photonic IOP biomonitoring method for glaucoma diagnosis, tested on pig eyes, shows promising results. Leveraging deep learning and speckle pattern analysis, together with the development of a prototype for human eyes testing, could enhance diagnosis and management, particularly in resource-constrained settings worldwide.

Full-optical photoacoustic imaging using speckle analysis and resolution enhancement by orthogonal pump patterns projection

This paper presents an approach for achieving full optical photoacoustic imaging with enhanced resolution utilizing speckle pattern analysis. The proposed technique involves projecting patterns derived from binary masks corresponding to orthogonal functions onto the target to elicit a photoacoustic signal. The resulting signal is then recorded using a high-speed camera and analyzed using correlation analysis of the speckle motion. Our results demonstrate the feasibility of this optical approach to achieve imaging with enhanced resolution without the need for physical contact with the target, opening up new possibilities for non-invasive medical imaging and other applications.

Remote sensing of human skin temperature by AI speckle pattern analysis

Analysis of dynamic differential speckle patterns, scattered from human tissues illuminated by a laser beam, has been found by many researchers to be applicable for noncontact sensing of various biomedical parameters. The COVID-19 global pandemic brought the need for massive rapid-remote detection of a fever in closed public spaces. The existing non-contact temperature measurement methods have a significant tradeoff between the measurement distance and accuracy. This paper aims to prove the feasibility of an accurate temperature measurement system based on speckle patterns analysis, enabling the sensing of human temperature from an extended distance greater than allowed by the existing methods. In this study, we used speckle patterns analysis combined with artificial intelligence (AI) methods for human temperature extraction, starting with fever/no fever binary classification and continuing with temperature measurement at higher resolution.

Evaluation of pitting corrosion by dynamic speckle pattern analysis

There is an increasing interest in non-destructive and real-time high-resolution approaches for corrosion studies in metals. In this paper, we propose the dynamic speckle pattern method as a low-cost, easy-to-implement, and quasi in-situ optical technique for the quantitative evaluation of pitting corrosion. This type of corrosion occurs in a specific area of a metallic structure and causes holes formation leading to structural failure. A Custom 450 stainless steel sample, placed in 3.5 wt% NaCl solution and applied to a 350 ,hbox {mV}_{SCE} potential to initiate the corrosion, is used as the sample. The speckle patterns formed by the scattering of a He-Ne laser light is changed over time due to any corrosion in the sample. The analysis of the time-integrate speckle pattern suggests that the growth rate of pitting decreases with time.

Optical sensor for remote estimation of CO2 concentration in the blood stream.

Speckle pattern analysis become a widespread method for remote sensing of various biomedical parameters. This technique is based on tracking the secondary speckle patterns reflected from a human skin illuminated by a laser beam. Speckle pattern variations can be translated into the corresponding partial carbon dioxide (CO2) state (High or Normal) in the bloodstream. We present a novel approach for remote sensing of human blood carbon dioxide partial pressure (PCO2) based on speckle pattern analyses combined with machine learning approach. The blood CO2 partial pressure is an important indicative parameter for a variety of malfunctions in the human body.

Detection of intralayer alignment in multicomponent lipids by dynamic speckle pattern analysis.

Multicomponent mixtures of bilayer lipids, thanks to the coexistence of liquid-crystalline phases in their structures, may be used in the development of functional membranes. In such membranes interlayer ordering distributes across membrane lamellae, resulting in long-range alignment of phase-separated domains. In this paper, we explore the dynamics of this phenomenon by laser speckle pattern analysis. We show that cholesterol content decreases the activity, and the rate of the domains size development is related to the change of physical roughness of the multicomponent lipid mixture. Our results are in agreement with the previous experimental reports. However, our experimental procedure is an easy-to-implement and effective methodology.

Two-dimensional electron beam size measurements with x-ray heterodyne near field speckles

In this paper we report on recent two-dimensional (2D) electron beam size measurements with a nonconventional synchrotron radiation interferometric technique based on x-ray heterodyne near field speckles (HNFS). The method relies on Fourier analysis of the random speckle patterns generated by a water suspension of nanospheres to assess the full 2D transverse coherence of the incoming x rays. The horizontal and vertical electron beam sizes are then retrieved by means of statistical optics approaches. The manuscript thoroughly describes the HNFS technique, and shows experimental results obtained at the ALBA Synchrotron Light Source. By changing the machine coupling, beam sizes as small as $5\text{ }\text{ }\ensuremath{\mu}\mathrm{m}$ are measured, thus improving on past measurements reported in the literature and proving the HNFS diagnostics suitable for low-emittance particle beams.

Optimizing ghost imaging via analysis and design of speckle patterns.

We study the influence rules of the speckle size of a light source on ghost imaging, and propose a type of speckle pattern to improve the quality of ghost imaging. The results show that image quality will first increase and then decrease with the increase in speckle size, and there is an optimal speckle size for a specific object. At the same time, by using a random distribution of speckle positions, a type of displacement speckle pattern is designed, and the imaging quality is better than that of random speckle patterns. These results are of great significance for finding the best speckle patterns suitable for detecting targets, which further promotes practical applications of ghost imaging.

Cross-polarization ratio analysis of digital speckle patterns applied to teeth demineralization diagnosis

We propose using the cross-polarization ratio (CPR) of the digital speckle pattern as a parameter to detect enamel erosion. We applied such analysis to successfully detect enamel erosion caused by teeth demineralization. The speckle patterns were produced using an in vitro study in human teeth where the erosion was produced artificially by acid attack. Our result is a proof of principle of the viability to apply CPR in the characterization of enamel erosion, which can be a powerful instrument for early diagnosis of teeth demineralization. This approach proved to be very sensitive to variation in speckle patterns compared with other traditional speckle patterns analyses.

Analysis of swallowing in infants and adults using speckle pattern analysis

The ability to detect and evaluate ingestion is especially important in toddlers. The development of new methods for detecting and accurately measuring ingestion is therefore extremely important. One of the methods allowing such measurements is speckle pattern analyses, a well-known phenomenon in coherent imaging. The method allows extraction of various medical parameters, such as blood pulse pressure, temporal signature of heartbeats and breath. The current work contains further development and application of the speckle tracking technique for remote detection and quantification of swallowing and distinguishing between sucking and swallowing to identify feeding disorders in infants and elderly individuals.

Comparative analysis of laser and simulated speckle pattern for single shot 3D reconstruction

Stereo photogrammetry is a fundamental technique for 3D measurements in different applications. As passive stereo methods struggle in texture-less regions, different light-coded patterns are projected to solve the correspondence problem. When the reconstruction method requires a series of sequentially coded light patterns, the system is prone to movement-related errors. A single pattern is utilized where the potential subjects are dynamic or change the position rapidly. A random speckle pattern is a good candidate for such systems. Primarily, there are two approaches to generate the speckle pattern in stereoscopic systems. The speckles can be generated by the coherent illumination of a laser diode, and the laser speckle-like pattern can also be simulated and projected by a professional projector. The purpose of both is to solve the stereo correspondence problem; however, the performance of both can differ, subjective to employed 3D sensing algorithm. In this Letter, we compare the performance of both for single-shot 3D reconstruction. We have discussed the pros and cons of both methods and analyze their ability to resolve long range 3D reconstruction.

Analysis of speckle patterns in electronic devices for the monitoring of current changes

Derived from new advances technological focused on the development of highly functional electronic devices; the manufacturers are obliged to the implementation of techniques to monitor their behavior. These techniques can be destructive or non-destructive, taking the analyzed device to the maximum working range. In this investigation report we show of the results obtained in the study of the speckle patterns of a positive voltage regulator, we used the probability density function and the correlation coefficients, as a statistical analysis of first and second order. The analysis was realized in each terminal of the electronic device, with changes in the input current. The result obtained suggest that the first and second order statistical analysis are good tool in a not destructive measurement and that its results can be relationship with the supply current. These changes are present a behavior ascending as the device operates in the junction temperature range; presenting itself as a mechanism for transmission and convection of heat, using the random distribution of speckle pattern, taking to optical measurement as an technique alternative in the study of electronic devices.

Remote photonic sensing of blood oxygen saturation via tracking of anomalies in micro-saccades patterns.

Speckle pattern analysis has been found by many researchers to be applicable to remote sensing of various biomedical parameters. This paper shows how analysis of dynamic differential speckle patterns scattered from subjects' sclera illuminated by a laser beam allows extraction of micro-saccades movement in the human eye. Analysis of micro-saccades movement using advanced machine learning techniques based on convolutional neural networks offers a novel approach for non-contact assessment of human blood oxygen saturation level (SpO2). Early stages of hypoxia can rapidly progress into pneumonia and death, and lives can be saved by advance remote detection of reduced blood oxygen saturation.

Emotion recognition using speckle pattern analysis and k-nearest neighbors classification

Emotion recognition is a basic communication tool in our daily interaction, and the recognition of emotions without contact and with high sensitivity may be very useful for various purposes. This paper presents a preliminary experimental investigation in which emotions of healthy subjects were recognized while they performed facial gestures related to those different emotions. Their faces were illuminated with a few laser spots and the formed back-scattered speckle patterns were analyzed with a camera having proper optics. By analyzing the temporal variation in the spatial distribution of those speckle patterns we estimated the muscles’ contraction–release motion in specific locations. The used data amount for the estimation procedure was less than 1% of the face frame so as to maintain the subjects’ privacy. Moreover, the presented optic method enables the detection of minor movements that cannot be recognized by the naked eye or conventional visual processing. After applying the machine learning k-nearest neighbors algorithm, we succeeded in reaching 89% accuracy in the recognition of emotions for the combination of two classification steps: subject recognition among the participants, and then emotion recognition among three optional emotions: happiness, sadness and neutral expression.

Multi-wavelength, handheld laser speckle imaging for skin evaluation.

A handheld device was developed and qualified for in vivo human skin evaluation using laser speckle imaging technology. Each laser speckle device prototype allows the choice of up to three different laser wavelengths in the range of 400nm to 800nm in total. Speckle pattern analysis gives various speckle parameters, for example, speckle contrast, speckle size, speckle modulation or fractal dimension. The developed laser speckle device prototypes were evaluated investigating three skin issues. We receive reproducible results from the speckle imaging device. For skin ageing, we found significant changes within three age groups. The effect of a methyl nicotinate treatment was clearly visible and quantifiable using a moorFLPI device as well as our speckle imaging device. In terms of basal cell carcinoma diagnosis, we found significant differences between normal and diseased skin, even though the number of samples was limited. As shown with first application examples, it was possible to demonstrate the potential of the method for skin evaluation in vivo.

Accurate real-time monitoring of high particulate matter concentration based on holographic speckles and deep learning

Accurate real-time monitoring of particulate matter (PM) has emerged as a global issue due to the hazardous effects of PM on public health and industry. However, conventional PM monitoring techniques are usually cumbersome and require expensive equipments. In this study, Holo-SpeckleNet is proposed as a fast and accurate PM concentration measurement technique with high throughput using a deep learning based holographic speckle pattern analysis. Speckle pattern datasets of PMs for a wide range of PM concentrations were acquired by using a digital in-line holography microscopy system. Deep autoencoder and regression algorithms were trained with the captured speckle pattern datasets to directly measure PM concentration from speckle pattern images without any air intake device and time-consuming post image processing. The proposed technique was applied to predict various PM concentrations using the test datasets, optimize hyperparameters, and compare its performance with a convolutional neural network (CNN) algorithm. As a result, high PM concentration values can be measured over air quality index of 150, above which human exposure is unhealthy. In addition, the proposed technique exhibits higher measurement accuracy and less overfitting than the CNN with a relative error of 7.46 ± 3.92%. It can be applied to design a compact air quality monitoring device for highly accurate and real-time measurement of PM concentrations under hazardous environment, such as factories or construction sites.

Measurement of Whole Blood Coagulation Time by Laser Speckle Pattern Correlation

We present here the results obtained by correlation analysis of digital speckle patterns to assess coagulation time in citrated whole blood. The spread of coagulation time values in experiments was no more than 25% for an essentially healthy cohort of laboratory animals. Studies of normal and pathological plasma showed that the method is able to detect pathological hemostasis. The ability to create a new universal optical coagulometer analyzing citrated and native blood, as well as whole blood, is discussed. Estimation of citrated blood clotting time is an intermediate stage in developing a test for native blood.