Electro-optical Sensors Research Articles

Quantitative measurement of the flow depending nasal valve function by elastography with electro-optical distance sensors. A pilot study

Nasal valve function depends on the intensity of the inspiratory nasal airflow, the geometry of the nasal entrance and the mechanical properties of the lateral nasal wall. It is desirable to obtain objective information on the relation between flow and valve movement. In this study, the deflection of the lateral nasal wall and the inspiratory flow were measured on 30 healthy volunteers, aged 18 to 82 without a history of severe trauma or nasal surgery. Electro-optical distance sensors were housed under a full-face protective mask attached to an analogue inspiratory flowmeter. The mean values for normal breathing were assessed at 675 [cm3/s] for the bilateral flow and −0.57 mm for the total movement. With forced breathing, the mean values for the flow of both nostrils were found to be 1434 cm3/s and for the total movement −1.21 mm. Statistically significant differences between normal and forced breathing were found in all participants and in both sexes, but no significant correlation by age. Electro-optical distance measurement, representing a novel technical way for the ‘elastography’ of the nasal valve should be added to advanced 4-phase-rhinomanometers.

Creation of multimodal digital twins with reflexive AGI multilogic and multisensory

Reflexive AGI multilogic, multimodality and multisensors are the basis for the multidisciplinary development of intelligent digital twins. Multimodality is implemented in several formats: text, image, speech, formulas, etc. Multilogic is implemented according to several rules or methods or ways of working with knowledge and data, and the criterion for selecting the best result from all implementations. AGI multilogic carries out judgment, understanding, perception, comparison, analysis, choice, etc. Understanding is realized using the technology of unified objectification of the ontology of subject areas for the implementation of specific activities. Multisensor systems are combined groups of electro-optical, spectroscopic and holographic sensors, and combined series of sensors, such as a thermal imager, color camera, low-light camera, laser rangefinder, laser designator, laser, pointer-illuminator and others. Multisensory systems help monitor the psyche and performance of a person at the level of medical indicators of his physical condition in the process of joint activities with digital twins. Multimodal digital twin with AGI multilogic and multisensory is good human assistant in many areas of activity.

Maritime Object Detection by Exploiting Electro-Optical and Near-Infrared Sensors Using Ensemble Learning

Maritime Object Detection by Exploiting Electro-Optical and Near-Infrared Sensors Using Ensemble Learning

Wearable electro-optical sensor for monitoring of nitrogen dioxide gas in environment

Environmental monitoring for detection of harmful pollutants is a challenge in today’s world. Development of wearable sensors with cost-effectiveness and practicality, while using accessible electronic components can be a solution to this challenge. In the present work ultraviolet light emitting diodes (UV-LEDs) and a Light Dependent Resistor (LDR) pair with carefully designed programmable circuitry is used to detect Nitrogen Dioxide (NO2) in the environment. The sensor is battery operated and rechargeable, making it convenient and eco-friendly. The sensor developed is miniaturized and wearable in nature for the detection of analyte gas in the environment. The sensor is stable and inexpensive as compared to the existing NO2 sensors available, which are electro-chemical, or oxide based. The sensor system is sensitive and selective to its exposure to NO2 gas and has no interference with typical environmental attributes such as humidity and temperature, which is a common challenge in gas detection technologies. The principle of gas detection is absorption based, light falling on the detector absorbed by the test gas present in the environment, thus changing the detector resistance. Absorption-based gas detection is indigenously simple but highly effective technique. This method not only ensures good sensitivity but enhances the selectivity of the electro-optical sensor towards the test gas preventing false triggering in the presence of other interfering gases.

Stretchable and Self-Powered Mechanoluminescent Triboelectric Nanogenerator Fibers toward Wearable Amphibious Electro-Optical Sensor Textiles.

Flexible electro-optical dual-mode sensor fibers with capability of the perceiving and converting mechanical stimuli into digital-visual signals show good prospects in smart human-machine interaction interfaces. However, heavy mass, low stretchability, and lack of non-contact sensing function seriously impede their practical application in wearable electronics. To address these challenges, a stretchable and self-powered mechanoluminescent triboelectric nanogenerator fiber (MLTENGF) based on lightweight carbon nanotube fiber is successfully constructed. Taking advantage of their mechanoluminescent-triboelectric synergistic effect, the well-designed MLTENGF delivers an excellent enhancement electrical signal of 200% and an evident optical signal whether on land or underwater. More encouragingly, the MLTENGF device possesses outstanding stability with almost unchanged sensitivity after stretching for 200%. Furthermore, an extraordinary non-contact sensing capability with a detection distance of up to 35cm is achieved for the MLTENGF. As application demonstrations, MLTENGFs can be used for home security monitoring, intelligent zither, traffic vehicle collision avoidance, and underwater communication. Thus, this work accelerates the development of wearable electro-optical textile electronics for smart human-machine interaction interfaces.

Theoretical Aspects of Creating Multimodal Digital Twins with AGI Multilogic and Multisensory

AGI multilogic, multimodality and multisensors are the basis for the multidisciplinary development of intelligent digital twins. Multimodality is implemented in several formats: text, image, speech, formulas, etc. Multilogic is implemented according to several rules or methods or ways of working with knowledge and data, and the criterion for selecting the best result from all implementations. AGI multilogic carries out judgment, understanding, perception, comparison, analysis, choice, etc. Understanding is realized using the technology of unified objectification of the ontology of subject areas for the implementation of specific activities. Multisensor systems are combined groups of electro-optical, spectroscopic and holographic sensors, and combined series of sensors, such as a thermal imager, color camera, low-light camera, laser rangefinder, laser designator, laser, pointer-illuminator and others. Multisensory systems help monitor the psyche and performance of a person at the level of medical indicators of his physical condition in the process of joint activities with digital twins. Multimodal digital twin with AGI multilogic and multisensory is good human assistant in many areas of activity.

Four-state Fano resonance electro-optical modulator and sensor based on double borophene-dielectric grating structure

In this paper, we propose a borophene-based double-dielectric-grating structure (BDDGS) to realize the Fano resonance electro-optical modulator and sensor. The Fano resonance originated from the coupling of two resonance modes excited by the upper borophene grating (UBG) and the lower borophene grating (LBG). The coupling mode theory (CMT) is utilized to fit the Fano transmission spectrum. The calculated result fits well with the simulated spectrum. We found the Fano resonance wavelengths exhibit a blue shift with increasing the carrier density of the borophene layer. Further, we utilize this property to achieve a Four-state Fano resonance electro-optical modulator. The working wavelengths of the ON-ON (11), ON–OFF (10), OFF–ON (01), and OFF-OFF (00) states are 1.321 μm and 1.676 μm, respectively. The asymmetric Fano spectra exhibit high sensing sensitivity S=21THz/RIU and figure of merit FOM=3.9RIU−1. Moreover, our proposed structure possesses a slow-fast light effect with the maximum group index are 57.84 and −332.3, respectively. Although several Fano resonance and electro-optical modulators based on two- dimensional (2D) material, like graphene, black phosphorus, and transition metal dichalcogenides (TMDs) are previously reported, this paper designed a borophene-based metamaterial to achieve Fano resonance electro-optical modulator and sensor, because borophene possesses a series of excellent physical and chemical properties, for example, mechanical compliance, high carrier mobility, and optical transparency. Therefore, the proposed borophene-based metamaterial will be beneficial in the fields of high-performance plasmonic sensors, slow light, and electro-optical modulators in the near future.

A Novel Proposal of an Electro-Optical Sensor to Measure Various Levels of an Electric Field Using Pockels Effect Photonic Crystals

Electro-optic sensors based on the Pockels effect are key components for optical instrumentation due to their speed and accuracy. In this work, we propose a new electro-optic platform using two-dimensional photonic crystals of gallium arsenide and barium titanate (GaAs-BTO). This structure allows for the measurement of various electric field levels, from 0 kV/mm to 50 kV/mm, with a very high sensitivity and a compact size. The opto-geometric parameters of this sensor were chosen, optimized, and analyzed numerically using the Plane Wave Expansion (PWE) and Finite-Difference Time-Domain (FDTD) methods.

Nonenzymatic dual glucose sensing on boronic acid modified zeolitic imidazolate framework-67 nanoparticles for diabetes management.

For early diabetes identification and management, the progression of an uncomplicated and exceedingly responsive glucose testing technology is crucial. In this study, we present a new sensor incorporating a composite of metal organic framework (MOF) based on cobalt, coated with boronic acid to facilitate selective glucose binding. Additionally, we successfully employed a highly sensitive electro-optical immunosensor for the detection of subtle changes in concentration of the diabetes biomarker glycated haemoglobin (HbA1c), using zeolitic imidazolate framework-67 (ZIF-67) coated with polydopamine which further modified with boronic acid. Utilizing the polymerization characteristics of dopamine and the NH2 groups, a bonding structure is formed between ZIF-67 and 4-carboxyphenylboronic acid. ZIF-67 composite served as an effective substrate for immobilising 4-carboxyphenylboronic acid binding agent, ensuring precise and highly selective glucose identification. The sensing response was evaluated through both electrochemical and optical methods, confirming its efficacy. Under optimized experimental condition, the ZIF-67 based sensor demonstrated a broad detection range of 50-500mg dL-1, a low limit of detection (LOD) of 9.87mg dL-1 and a high correlation coefficient of 0.98. Furthermore, the 4-carboxyphenylboronic acid-conjugated ZIF-67-based sensor platform exhibited remarkable sensitivity and selectivity in optical-based detection for glycated haemoglobin within the clinical range of 4.7-11.3%, achieving a LOD of 3.7%. These findings highlight the potential of the 4-carboxyphenylboronic acid-conjugated ZIF-67-based electro-optical sensor as a highly sensitive platform for diabetes detection.

Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection.

On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%-93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.

Mechanism Analysis of Thermal-Induced Periodic Fluctuations in Electro-Optic Sensor

Mechanism Analysis of Thermal-Induced Periodic Fluctuations in Electro-Optic Sensor

Wavelength Tunable Infrared Perfect Absorption in Plasmonic Nanocrystal Monolayers.

The ability to efficiently absorb light in ultrathin (subwavelength) layers is essential for modern electro-optic devices, including detectors, sensors, and nonlinear modulators. Tailoring these ultrathin films' spectral, spatial, and polarimetric properties is highly desirable for many, if not all, of the above applications. Doing so, however, often requires costly lithographic techniques or exotic materials, limiting scalability. Here we propose, demonstrate, and analyze a mid-infrared absorber architecture leveraging monolayer films of nanoplasmonic colloidal tin-doped indium oxide nanocrystals (ITO NCs). We fabricate a series of ITO NC monolayer films using the liquid-air interface method; by synthetically varying the Sn dopant concentration in the NCs, we achieve spectrally selective perfect absorption tunable between wavelengths of two and five micrometers. We achieve monolayer thickness-controlled coupling strength tuning by varying NC size, allowing access to different coupling regimes. Furthermore, we synthesize a bilayer film that enables broadband absorption covering the entire midwave IR region (λ = 3-5 μm). We demonstrate a scalable platform, with perfect absorption in monolayer films only hundredths of a wavelength in thickness, enabling strong light-matter interaction, with potential applications for molecular detection and ultrafast nonlinear optical applications.

Improve Ocean Sensing Using Unmanned Aerial Vehicles

Abstract This paper discusses the day and night capability of a newly developed optical sensing suite flown on manned and unmanned aircraft in Monterey Bay and Santa Barbara, CA, during 2022 to detect marine mammals. New developments of electro-optical sensors integrated on medium and long-endurance unmanned aerial vehicles (UAVs) can provide cost-effective, qualitative sensing of marine biodiversity, to include species identification. Processing of captured imagery in both day and night applications has proven effective in removing surface-level reflections and background noise, which allows sensing of subsurface marine life to several attenuation lengths. Leveraging bioluminescence as a natural image enhancement phenomenon along with advanced signal processing of imagerycaptured by this sensing suite improves the quality of nighttime detection and species identification. Concepts and applications for this sensing suite integrated on UAV platforms for ocean food-chain modeling and maritime traffic avoidance are discussed, including deployment strategies for the Channel IslandsNational Marine Sanctuary off the California coast.

A High-Precision Detection Model of Small Objects in Maritime UAV Perspective Based on Improved YOLOv5

Object detection by shipborne unmanned aerial vehicles (UAVs) equipped with electro-optical (EO) sensors plays an important role in maritime rescue and ocean monitoring. However, high-precision and low-latency maritime environment small-object-detection algorithms remain a major challenge. To address this problem, this paper proposes the YOLO-BEV (“you only look once”–“bird’s-eye view”) model. First, we constructed a bidirectional feature fusion module—that is, PAN+ (Path Aggregation Network+)—adding an extremely-small-object-prediction head to deal with the large-scale variance of targets at different heights. Second, we propose a C2fSESA (Squeeze-and-Excitation Spatial Attention Based on C2f) module based on the attention mechanism to obtain richer feature information by aggregating features of different depth layers. Finally, we describe a lightweight spatial pyramid pooling structure called RGSPP (Random and Group Convolution Spatial Pyramid Pooling), which uses group convolution and random channel rearrangement to reduce the model’s computational overhead and improve its generalization ability. The article compares the YOLO-BEV model with other object-detection algorithms on the publicly available MOBDrone dataset. The research results show that the mAP0.5 value of YOLO-BEV reached 97.1%, which is 4.3% higher than that of YOLOv5, and the average precision for small objects increased by 22.2%. Additionally, the YOLO-BEV model maintained a detection speed of 48 frames per second (FPS). Consequently, the proposed method effectively balances the accuracy and efficiency of object-detection in shipborne UAV scenarios, outperforming other related techniques in shipboard UAV maritime object detection.

Laser Safety-What Is the Laser Hazard Distance for an Electro-Optical Imaging System?

Laser safety is an important topic. Everybody working with lasers has to follow the long-established occupational safety rules to prevent people from eye damage by accidental irradiation. These rules comprise, for example, the calculation of the Maximum Permissible Exposure (MPE), as well as the corresponding laser hazard distance, the so-called Nominal Ocular Hazard Distance (NOHD). At exposure levels below the MPE, laser eye dazzling may occur and is described by a quite new concept, leading to definitions such as the Maximum Dazzle Exposure (MDE) and to its corresponding Nominal Ocular Dazzle Distance (NODD). In earlier work, we defined exposure limits for sensors corresponding to those for the human eye: The Maximum Permissible Exposure for a Sensor, MPES, and the Maximum Dazzle Exposure for a Sensor, MDES. In this publication, we report on our continuative work concerning the laser hazard distances arising from these exposure limits. In contrast to the human eye, unexpected results occur for electro-optical imaging systems: For laser irradiances exceeding the exposure limit, MPES, it can happen that the laser hazard zone does not extend directly from the laser source, but only from a specific distance to it. This means that some scenarios are possible where an electro-optical imaging sensor may be in danger of getting damaged within a certain distance to the laser source but is safe from damage when located close to the laser source. This is in contrast to laser eye safety, where it is assumed that the laser hazard zone always extends directly from the laser source. Furthermore, we provide closed-form equations in order to estimate laser hazard distances related to the damaging and dazzling of the electro-optical imaging systems.

Measurement of high-power transient electromagnetic pulse field with integrated photonic electric-field sensor

To satisfy certain special requirements for high-power transient electromagnetic pulse (EMP) field measurements, such as high electric field intensity, broad bandwidth, fast rise time, and compact size, here is described an integrated electro-optic sensor. The frequency response in the range from 100 kHz to 12 GHz and the time-domain input/output characteristics of a sub-nanosecond EMP field with an intensity from 5 to 100 kV/m were experimentally characterized. The sensor was demonstrated to measure the time-domain waveform of the ultrawideband EMP field signals with a rise time of 0.35 ns and bandwidth of 1 GHz. In addition, the minimal detectable electric field strength was 16 mV/m/Hz1/2 in the frequency domain, and an intense sub-nanosecond electromagnetic pulse with a peak amplitude of 100 kV/m was observed in the time domain. The reported sensor is expected to be broadly used for intense EMP measurements.

The concept of sUAS/DL-based system for detecting and classifying abandoned small firearms

Military object detection and identification is a key capability in surveillance and reconnaissance. It is a major factor in warfare effectiveness and warfighter survivability. Inexpensive, portable, and rapidly deployable small unmanned aerial systems (sUAS) in conjunction with powerful deep learning (DL) based object detection models are expected to play an important role for this application. To prove overall feasibility of this approach, this paper discusses some aspects of designing and testing of an automated detection system to locate and identify small firearms left at the training range or at the battlefield. Such a system is envisioned to involve an sUAS equipped with a modern electro-optical (EO) sensor and relying on a trained convolutional neural network (CNN). Previous study by the authors devoted to finding projectiles on the ground revealed certain challenges such as small object size, changes in aspect ratio and image scale, motion blur, occlusion, and camouflage. This study attempts to deal with these challenges in a realistic operational scenario and go further by not only detecting different types of firearms but also classifying them into different categories. This study used a YOLOv2 CNN (ResNet-50 backbone network) to train the model with ground truth data and demonstrated a high mean average precision (mAP) of 0.97 to detect and identify not only small pistols but also partially occluded rifles.

Passive Electro-Optical Tracking of Resident Space Objects for Distributed Satellite Systems Autonomous Navigation

Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At present, RSO are tracked and catalogued using ground-based observations, but space-based space surveillance (SBSS) represents a valid alternative (or complementary asset) due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. This paper proposes a particle swarm optimization (PSO) algorithm for DSS AN and manoeuvring, specifically addressing RSO tracking and collision avoidance requirements as an integral part of the overall system design. More specifically, a DSS architecture employing hyperspectral sensors for Earth observation is considered, and passive electro-optical sensors are used, in conjunction with suitable mathematical algorithms, to accomplish autonomous RSO tracking and classification. Simulation case studies are performed to investigate the tracking and system collision avoidance capabilities in both space-based and ground-based tracking scenarios. Results corroborate the effectiveness of the proposed AN technique and highlight its potential to supplement either conventional (ground-based) or SBSS tracking methods.

Calibrating a Radio Frequency Electrooptic Sensor for Field-Relevant Temperature Conditions in a Laboratory Setting

Legacy high-powered electric field measurement devices, such as flush plate dipoles (D-dots) and waveguides, are the primary diagnostic tools used for free-field experimentation. Unfortunately, these metallic devices perturb the electric field of interest via scattering and require the use of coaxial cables (fluctuate with temperature). Electrooptic (EO) probes offer a potential solution as they minimally perturb the electric field, and fiber-optic cables can be designed to correct or prevent temperature fluctuations. However, to replace the legacy diagnostics, it is critical that the EO probes are calibrated in field-relevant conditions (outdoor temperatures between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4~^{\circ} \text{C}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$49~^{\circ} \text{C}$ </tex-math></inline-formula> ). Characterization of a modern EO sensor is done by using an environmental chamber and a gigahertz transverse electromagnetic (GTEM) cell. The collected data in this article indicates that modern <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {LiNbO}_{{3}}$ </tex-math></inline-formula> EO sensors have linear changes across both various temperatures and electric fields. This article outlines the process of creating field-relevant conditions in laboratory settings. Because temperature effects are ubiquitous with most devices, the temperature response of the EO probe needed to be isolated from all other temperature effects. Using a novel way of applying the design of experiments (DoE) method, the temperature response of the EO probe can be isolated and a linear trend can be resolved. The data suggest that the EO probe under test can be used to measure RF fields as long as a small correction factor is applied to 0.52% per °C for every °C away from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22.0~^{\circ} \text{C}$ </tex-math></inline-formula> in either direction.

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electro-optical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The article also proposes the direction of future research for sea-surface object detection based on EO sensors.