Vibrational Imaging Research Articles

Widely tunable fiber optical parametric oscillator synchronized with a Ti:sapphire laser for stimulated Raman scattering microscopy

Stimulated Raman scattering (SRS) microscopy is a powerful vibrational imaging technique with high chemical specificity. However, the insufficient tuning range or speed of light sources limits the spectral range of SRS imaging and, hence, the ability to identify molecular species. Here, we present a widely tunable fiber optical parametric oscillator with a tuning range of 1470 cm−1, which can be synchronized with a Ti:sapphire laser. By using the synchronized light sources, we develop an SRS imaging system that covers the fingerprint and C–H stretching regions, without balanced detection. We validate its broadband imaging capability by visualizing a mixed polymer sample in multiple vibrational modes. We also demonstrate SRS imaging of HeLa cells, showing the applicability of our SRS microscope to biological samples.

Cyano-Hydrol green derivatives: Expanding the 9-cyanopyronin-based resonance Raman vibrational palette

9-cyanopyronin is a promising scaffold that exploits resonance Raman enhancement to enable sensitive, highly multiplexed biological imaging. Here, we developed cyano-Hydrol Green (CN-HG) derivatives as resonance Raman scaffolds to expand the color palette of 9-cyanopyronins. CN-HG derivatives exhibit sufficiently long wavelength absorption to produce strong resonance Raman enhancement for near-infrared (NIR) excitation, and their nitrile peaks are shifted to a lower frequency than those of 9-cyanopyronins. The fluorescence of CN-HG derivatives is strongly quenched due to the lack of the 10th atom, unlike pyronin derivatives, and this enabled us to detect spontaneous Raman spectra with high signal-to-noise ratios. CN-HG derivatives are powerful candidates for high performance vibrational imaging.

Experimental investigation of the emission and performance of preheated and unheated garlic methyl ester fuels and diesel fuel in a diesel engine

It is thought that vehicles running on diesel fuel significantly impact climate change in the world. To reduce this negative effect, changing the fuel source without making any changes to the engines is a very attractive method. Considering the environment, climate change, living health, and the lifespan of fossil fuels, studies on biodiesel fuels, which can be an alternative to diesel fuel, are important. In the present study, garlic methyl ester (GME) was produced from crude garlic oil using the transesterification method. The impacts of preheated and unheated GME and its blends on a diesel engine with an air cooling system without any modification were researched. GME was used with diesel at 20% and 60% volumetric ratios and was also used at 100%. Experimental studies were conducted at five engine loads (1, 1.5, 2, 2.5, and 3 kW). The GME and its blends were tested at the specified loads, and the results were obtained. The GME and blends were then preheated at 70 °C, and the experimental studies were repeated under the same conditions. In the tests, NOx, CO, CO2, HC, smoke density, EGT, BSFC, in-cylinder pressure, engine torque, noise, vibration, and thermal image analyses were conducted. According to the results, CO 22.3%, HC 24.9%, smoke density 25.6%, torque 4.6%, cylinder pressure 5.2%, and EGT 11.5% decreased, whereas BSFC 11.2%, noise 1.4%, vibration 3.8%, CO2 6%, and NOx 18.2% emissions increased in GME and its blends compared to diesel. The highest measurement values were obtained with 100% GME fuel. It was observed that both increasing the GME ratio and the preheating process improved the emission and performance values. The results obtained show that GME and its blends can be easily used in diesel engines without any modification.

Research on surface roughness detection and prediction of ti-6Al-4v titanium alloy based on multi-feature fusion

Considering that titanium alloy is a critical aviation resource, ensuring its machining quality is of significant importance. Surface roughness remains a key parameter for surface quality inspection. This article introduces a multi-sensor titanium alloy milling monitoring system aimed at accurately monitoring surface quality during titanium alloy processing. Principal component analysis is conducted on three-dimensional milling force and vibration. We propose a multi-objective cutting parameter optimization procedure to simultaneously optimize multiple cutting parameters to improve surface roughness and tool life. Considering the high dependence of visual measurement on the light source, we strive to mitigate this limitation and improve prediction accuracy by considering one-dimensional and two-dimensional feature values. We establish a multidimensional signal feature surface roughness prediction system based on milling parameters, milling vibration, milling force, and texture image features. Using particle swarm algorithm and machine learning models, the undetermined parameters in the prediction system are obtained. The results show that the prediction accuracy of the multi-signal feature fusion surface roughness prediction system is 99.12%, with a mean square error of less than 0.01. The research can provide some theoretical guidance for the accurate monitoring of the surface quality of titanium alloy processing.

Прогноз суицидального риска у сотрудников силовых ведомств, в том числе военнослужащих

Suicide is a major medical and social concern for law enforcement, a contemporary army included, not only in Russia but abroad as well. In recent years, frequency of suicides and suicidal attempts has been growing among law enforcement officials (LEOs), military personnel included. Therefore, it seems relevant to develop a model for predicting suicide risk. In this study, our aim was to develop a model for predicting suicide risk in LEOs based on express testing results. Our research object was represented by LEOs (n = 591), their average age being 23.71 ± 1.12 years. To assess suicide risk, we used a questionnaire for suicide risk assessment ‘SSR-2’, which is a part of DAP-2 methodology for deviant behavior research, and a clinical-psychopathological method. LEOs’ personality characteristics and their current psychophysiological state were identified by using vibration imaging, a technology for recording and mathematically analyzing micro-vibrations of the head and face. It has certain advantages over its analogues. We determined psychophysiological characteristics, basic abilities (types of Gardner’s multiple intelligence) and moral qualities that differed in people with elevated suicide risk against the control. We identified a difference between unconscious reactions of the examined people to stimuli and declared (conscious) ones, which indicates that LEOs tend to hide any signs of suicidal behavior in them. A mathematical model was built for predicting suicide risk: we developed an integral suicide risk assessment and created a probabilistic nomogram that makes it possible to establish likelihood of suicidal behavior signs with accuracy above 98 % relying on results obtained by a 5-minute express test. Use of this predictive model helps identify those people among personnel who should undergo a profound check-up by a psychological support team. Our research results can serve as a basis for creating an objective concept for diagnostics of suicide risk factors in LEOs.

Prognosis of suicidal risk among law enforcement officials including military personnel

Suicide is a major medical and social concern for law enforcement, a contemporary army included, not only in Russia but abroad as well. In recent years, frequency of suicides and suicidal attempts has been growing among law enforcement officials (LEOs), military personnel included. Therefore, it seems relevant to develop a model for predicting suicide risk. In this study, our aim was to develop a model for predicting suicide risk in LEOs based on express testing results. Our research object was represented by LEOs (n = 591), their average age being 23.71 ± 1.12 years. To assess suicide risk, we used a questionnaire for suicide risk assessment ‘SSR-2’, which is a part of DAP-2 methodology for deviant behavior research, and a clinical-psychopathological method. LEOs’ personality characteristics and their current psychophysiological state were identified by using vibration imaging, a technology for recording and mathematically analyzing micro-vibrations of the head and face. It has certain advantages over its analogues. We determined psychophysiological characteristics, basic abilities (types of Gardner’s multiple intelligence) and moral qualities that differed in people with elevated suicide risk against the control. We identified a difference between unconscious reactions of the examined people to stimuli and declared (conscious) ones, which indicates that LEOs tend to hide any signs of suicidal behavior in them. A mathematical model was built for predicting suicide risk: we developed an integral suicide risk assessment and created a probabilistic nomogram that makes it possible to establish likelihood of suicidal behavior signs with accuracy above 98 % relying on results obtained by a 5-minute express test. Use of this predictive model helps identify those people among personnel who should undergo a profound check-up by a psychological support team. Our research results can serve as a basis for creating an objective concept for diagnostics of suicide risk factors in LEOs.

A single-stage anchor-free rotating target visual detection algorithm suitable for flexible body vibration displacement measurement

The phenomenon of angular inclination of flexible structures during vibration poses a significant challenge to the applicability of visual vibration measurement methods because the target locked in the captured image will produce unknown geometric deformations such as scale, displacement, and angle in the time domain space, and the horizontal rectangular frame used for matching during target detection will also increase the false detection rate of the target due to the introduction of more background information. Such subtle geometric deformations and false detections can lead to severe fit errors in the displacement curves regressed by the visual vibration measurement algorithm. To effectively improve the accuracy and robustness of vibration image target recognition, this article takes the flexible body captured by a high-speed camera as the target of vibration displacement measurement. It introduces the rotating target detection method based on deep learning into the field of visual vibration measurement, which verifies the feasibility of the deep learning method in flexible body vibration measurement, and based on the deep convolutional neural network framework, a high-precision displacement measurement algorithm based on single-stage anchor-free rotating target detection is proposed. The algorithm in this article first uses the CSPDarknet backbone network to extract multi-scale features of flexible structural image sequences. It then uses PANet to fuse the top-down and bottom-up bidirectional feature maps of the four bridge target feature maps obtained through the backbone network. The shallow and deep information is used for semantic feature fusion and combined with the Coordinate Attention mechanism to achieve target finding and fine positioning on the feature map. Finally, we use the coordinates of the bounding box obtained from the test to regress the position offset of the object’s center point. To verify the accuracy of the algorithm in this article, we conducted experimental validation on the cable-stayed bridge model and the actual bridge and compared the performance with the traditional template matching algorithm, differential optical flow method, and various deep learning algorithms with different localization principles, as well as the displacement signals collected and processed by accelerometers. The experimental results of time-frequency characteristics analysis show that the vibration displacement trajectories regressed by the algorithm in this paper have the best overlap with the displacement measurements collected by the accelerometer, which verifies that the algorithm in this article has good application potential and implementation space in the field of condition monitoring of flexible structural bodies.

Phase-shifting optothermal microscopy enables live-cell mid-infrared hyperspectral imaging of large cell populations at high confluency.

Rapid live-cell hyperspectral imaging at large fields of view (FOVs) and high cell confluency remains challenging for conventional vibrational spectroscopy-based microscopy technologies. At the same time, imaging at high cell confluency and large FOVs is important for proper cell function and statistical significance of measurements, respectively. Here, we introduce phase-shifting mid-infrared optothermal microscopy (PSOM), which interprets molecular-vibrational information as the optical path difference induced by mid-infrared absorption and can take snapshot vibrational images over broad excitation areas at high live-cell confluency. By means of phase-shifting, PSOM suppresses noise to a quarter of current optothermal microscopy modalities to allow capturing live-cell vibrational images at FOVs up to 50 times larger than state of the art. PSOM also reduces illumination power flux density (PFD) down to four orders of magnitude lower than other conventional vibrational microscopy methods, such as coherent anti-Stokes Raman scattering (CARS), thus considerably decreasing the risk of cell photodamage.

Endogenous Production and Vibrational Analysis of Heavy-Isotope-Labeled Peptides from Cyanobacteria.

Stable isotope labeling is an extremely useful tool for characterizing the structure, tracing the metabolism, and imaging the distribution of natural products in living organisms using mass-sensitive measurement techniques. In this study, a cyanobacterium was cultured in 15 N/13 C-enriched media to endogenously produce labeled, bioactive oligopeptides. The extent of heavy isotope incorporation in these peptides was determined with LC-MS, while the overall extent of heavy isotope incorporation in whole cells was studied with nanoSIMS and AFM-IR. Up to 98 % heavy isotope incorporation was observed in labeled cells. Three of the most abundant peptides, microcystin-LR (MCLR), cyanopeptolin-A (CYPA), and aerucyclamide-A (ACAA), were isolated and further studied with Raman and FTIR spectroscopies and DFT calculations. This revealed several IR and Raman active vibrations associated with functional groups not common in ribosomal peptides, like diene, ester, thiazole, thiazoline, and oxazoline groups, which could be suitable for future vibrational imaging studies. More broadly, this study outlines a simple and relatively inexpensive method for producing heavy-labeled natural products. Manipulating the bacterial culture conditions by the addition of specific types and amounts of heavy-labeled nutrients provides an efficient means of producing heavy-labeled natural products for mass-sensitive imaging studies.

VIBRANT: spectral profiling for single-cell drug responses.

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Multispectral vibrational imaging for high resolution tracking of live cell metabolic processes

Multispectral vibrational imaging for high resolution tracking of live cell metabolic processes

A novel characterisation method for asphalt pavement structural performance assessment based on vehicle vibration data fusion

ABSTRACT Rapid structural performance assessment is essential to ensure asphalt pavement condition and efficient road network operation. However, extracting deeper pavement structural performance information based on vehicle vibration remains a major challenge. This study proposes a vibration data fusion method to realise the task of accurate structural performance characterisation. A 2D reconstruction algorithm transforms raw vibration signals into vibration images. Based on reconstruction images generated from different sensors and sensing directions, a pixel-level fusion framework and a feature-level fusion framework were put to improve the characterization accuracy. The study extracted two effective image texture features to characterise asphalt pavement’s structural strength. Fitting with deflection data, the average goodness-of-fit of optimised indicators combination under a feature-level fusion framework can reach 0.827. It is 32.5% and 15.2% higher than two unfused single-index accuracy. The data fusion framework shows the practicality in evaluating pavement structural performance rapidly and accurately through the vehicle vibration response.

Surmounting photon limits and motion artifacts for biological dynamics imaging via dual-perspective self-supervised learning

Visualizing rapid biological dynamics like neuronal signaling and microvascular flow is crucial yet challenging due to photon noise and motion artifacts. Here we present a deep learning framework for enhancing the spatiotemporal relations of optical microscopy data. Our approach leverages correlations of mirrored perspectives from conjugated scan paths, training a model to suppress noise and motion blur by restoring degraded spatial features. Quantitative validation on vibrational calcium imaging validates significant gains in spatiotemporal correlation (2.2×), signal-to-noise ratio (9–12 dB), structural similarity (6.6×), and motion tolerance compared to raw data. We further apply the framework to diverse in vivo experiments from mouse cerebral hemodynamics to zebrafish cardiac dynamics. This approach enables the clear visualization of the rapid nutrient flow (30 mm/s) in microcirculation and the systolic and diastolic processes of heartbeat (2.7 cycle/s), as well as cellular and vascular structure in deep cortex. Unlike techniques relying on temporal correlations, learning inherent spatial priors avoids motion-induced artifacts. This self-supervised strategy flexibly enhances live microscopy under photon-limited and motion-prone regimes.

Super-Resolution Stimulated Raman Scattering Microscopy with Graphical User Interface-Supported A-PoD.

Raman microscopy is a vibrational imaging technology that can detect molecular chemical bond vibrational signals. Since this signal is originated from almost every vibrational mode of molecules with different vibrational energy levels, it provides spatiotemporal distribution of various molecules in living organisms without the need for any labeling. The limitations of low signal strength in Raman microscopy have been effectively addressed by incorporating a stimulated emission process, leading to the development of stimulated Raman scattering (SRS) microscopy. Furthermore, the issue of low spatial resolution has been resolved through the application of computational techniques, specifically image deconvolution. In this article, we present a comprehensive guide to super-resolution SRS microscopy using an Adam-based pointillism deconvolution (A-PoD) algorithm, complemented by a user-friendly graphical user interface (GUI). We delve into the crucial parameters and conditions necessary for achieving super-resolved images through SRS imaging. Additionally, we provide a step-by-step walkthrough of the preprocessing steps and the use of GUI-supported A-PoD. This complete package offers a user-friendly platform for super-resolution SRS microscopy, enhancing the versatility and applicability of this advanced microscopy technique to reveal nanoscopic multimolecular nature. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Super-resolution stimulated Raman scattering microscopy with graphical user interface-supported A-PoD Support Protocol: Deuterium labeling on cells with heavy water for metabolic imaging.

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, Deep Learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signals and images, accounting for 80% of the literature studied. Secondly, the popular DL algorithm types and application areas are reviewed, of which CNN accounts for 60%. Then, this article carefully analyzes the specific functions of DL application for SHM based on the facility’s characteristics. The most scrutinized study focused on cracks, accounting for 30 percent of research papers. Finally, challenges and trends in applying DL for SHM are discussed. Among the trends, the Structural Health Monitoring Digital Twin (SHMDT) model framework is suggested in response to the trend of strong coupling between SHM technology and Digital Twin (DT), which can advance the digitalization, visualization, and intelligent management of SHM.

Stimulated Raman photothermal microscopy toward ultrasensitive chemical imaging.

Stimulated Raman scattering (SRS) microscopy has shown enormous potential in revealing molecular structures, dynamics, and couplings in complex systems. However, the sensitivity of SRS is fundamentally limited to the millimolar level due to shot noise and the small modulation depth. To overcome this barrier, we revisit SRS from the perspective of energy deposition. The SRS process pumps molecules to their vibrationally excited states. The subsequent relaxation heats up the surroundings and induces refractive index changes. By probing the refractive index changes with a laser beam, we introduce stimulated Raman photothermal (SRP) microscopy, where a >500-fold boost of modulation depth is achieved. The versatile applications of SRP microscopy on viral particles, cells, and tissues are demonstrated. SRP microscopy opens a way to perform vibrational spectroscopic imaging with ultrahigh sensitivity.

Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks.

A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%.

Bispectral analysis and information fusion technique for bearing fault classification

The feasibility and effectiveness of data fusion for the fault classification of bearing faults have been very well iterated in the literature. However, all previous endeavors have been limited to time, frequency, and time-frequency domain techniques. The use of higher-order spectral analysis (HOSA), especially Bispectrum and Trispectrum, for fault detection is gaining importance in recent studies due to the many advantages of HOSA. Bispectral features provide a valuable tool for capturing higher-order statistical relationships in signals, making them particularly effective in detecting nonlinearities and distinguishing between Gaussian and non-Gaussian data. Their robustness to noise and ability to reveal hidden information make them advantageous in applications such as vibration analysis, speech recognition, and image processing, where complex signal interactions and nonlinearity play a significant role in data interpretation and pattern recognition. This paper proposes a methodology for the fusion of the data from the vibration and the acoustic sensors for the fault detection of roller element bearings using bispectral features. Higher-order spectral characteristics are derived from vibration and acoustic sensor data, and they are fused using artificial neural networks and various other machine learning algorithms like support vector machine, K nearest neighbor, Naïve Bayes algorithm, and decision tree. This work primarily aims to evaluate the performance of each classifier when applied to the fused data, in contrast to the performance when using individual sensor data alone. The outcomes revealed that, even though the accuracy of the acoustic sensor data was lower in comparison to the vibration sensor data, which exhibited the highest performance of 100% accuracy with nearly all the classifiers, the fused data achieved remarkable results of 100% accuracy with artificial neural networks and decision trees. However, the Naïve Bayes algorithm yielded the lowest accuracy when applied to the fused data. The primary objective of this paper is to demonstrate the application of bispectrum analysis for data fusion and to enhance confidence in fault detection. It achieves this by maintaining the capability to accurately and dependably detect faults, even when a single sensor encounters issues or falls short of anticipated performance standards.

Defect Detection in Carbon Fiber-Reinforced Plate by Imaging of Mechanical Nonlinearity-Induced Sideband Vibrations

Laser Doppler scanning vibrometry is used for imaging spectral vibration components in a carbon fiber-reinforced composite plate that contains a sub-surface delamination defect caused by hammer impact. The images reveal sideband generation at the location of the defect, reflecting mechanical nonlinearity-induced mixing between a high amplitude, low-frequency vibration that modulates the stress–strain behavior near the defect and a low amplitude, high-frequency probe vibration. In this work, a multifrequency probe is used to tackle the problem that the mixing coefficients are, in practice, frequency dependent. Based on the measured sideband amplitudes, a study is presented on the expected feasibility of detecting defects by a full field imaging scheme based on a photorefractive interferometer that is configured as a vibrometer acting as a bandpass filter around a sideband frequency of interest.

Spatially Resolved Chiroptical Spectroscopies Emphasizing Recent Applications to Thin Films of Chiral Organic Dyes.

Instrumental techniques able to identify and structurally characterize the aggregation states in thin films of chiral organic π-conjugated materials, from the first-order supramolecular arrangement up to the microscopic and mesoscopic scale, are very helpful for clarifying structure-property relationships. Chiroptical imaging is currently gaining a central role, for its ability of mapping local supramolecular structures in thin films. The present review gives an overview of electronic circular dichroism imaging (ECDi), circularly polarized luminescence imaging (CPLi), and vibrational circular dichroism imaging (VCDi), with a focus on their applications on thin films of chiral organic dyes as case studies.