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This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields. [ABSTRACT FROM AUTHOR]

LiDAR sensors, pivotal in various fields like agriculture and robotics for tasks such as 3D object detection and map creation, are increasingly coupled with thermal cameras to harness heat information. This combination proves particularly effective in adverse conditions like darkness and rain. Ensuring seamless fusion between the sensors necessitates precise extrinsic calibration. Our innovative calibration method leverages human presence during sensor setup movements, eliminating the reliance on dedicated calibration targets. It optimizes extrinsic parameters by employing a novel evolutionary algorithm on a specifically designed loss function that measures human alignment across modalities. Our approach showcases a notable 4.43 % improvement in the loss over extrinsic parameters obtained from target-based calibration in the FieldSAFE dataset. This advancement reduces costs related to target creation, saves time in diverse pose collection, mitigates repetitive calibration efforts amid sensor drift or setting changes, and broadens accessibility by obviating the need for specific targets. The adaptability of our method in various environments, like urban streets or expansive farm fields, stems from leveraging the ubiquitous presence of humans. Our method presents an efficient, cost-effective, and readily applicable means of extrinsic calibration, enhancing sensor fusion capabilities in the critical fields reliant on precise and robust data acquisition. [ABSTRACT FROM AUTHOR]

Self-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capability for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking 'guided' dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in which the exposed time window of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over twenty times, while the total laser cycles in each frame are reduced by at least six times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar. [ABSTRACT FROM AUTHOR]

LiDAR and camera are pivotal sensors of 3D (three-dimensional) object detection. As a result of their different characteristics, increasingly multimodal-based object detection methods have been proposed. Now, popular methods are to hardly associate camera features with LiDAR features, but the features are frequently enhanced and aggregated, so there is a major challenge in how to align two features effectively. Therefore, we propose CAF-RCNN. On the basis of PointRCNN, using Feature Pyramid Network (FPN) to extract advanced semantic features at different scales, then fusing these features with the LiDAR features of the Set Abstraction (SA) module output in PointRCNN and subsequent steps. Regarding the features fusion module, we design a module based on the cross-attention mechanism, CAFM (Cross-Attention Fusion Module). It combines two channel attention streams in a cross-over fashion to utilize rich details about significant objects in the Image Stream and Geometric Stream. We did a lot of experiments on the KITTI dataset, and the result shows that our method is 6.43% higher than PointRCNN in 3D accuracy. [ABSTRACT FROM AUTHOR]

Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explored the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with a 360° field of view obtained with lidar sensors by encoding either depth, reflectivity, or near-infrared light in the image pixels. We showed that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions where vision sensors present inherent limitations. We provided both a qualitative and quantitative analysis of the performance of a variety of neural network architectures. We believe that using DL models built for visual cameras offers significant advantages due to their much wider availability and maturity compared to point cloud-based perception. [ABSTRACT FROM AUTHOR]

Recently, 3D object detection based on multi-modal sensor fusion has been increasingly adopted in automated driving and robotics. For example, the semantic information provided by cameras and the geometric information provided by light detection and ranging (LiDAR) are fused to perceive 3D objects, as single modal sensors are unable to capture enough information from the environment. Many state-of-the-art methods fuse the signals sequentially for simplicity. By sequentially, we mean using the image semantic signals as auxiliary input for LiDAR-based object detectors would make the overall performance heavily rely on the semantic signals. Moreover, the error introduced by these signals may lead to detection errors. To remedy this dilemma, we propose an approach coined supervised-PointRendering to correct the potential errors in the image semantic segmentation results by training auxiliary tasks with fused features of the laser point geometry feature, the image semantic feature and a novel laser visibility feature. The laser visibility feature is obtained through the raycasting algorithm and is adopted to constrain the spatial distribution of fore- and background objects. Furthermore, we build an efficient anchor-free Single Stage Detector (SSD) powered by an advanced global-optimal label assignment to achieve a better time–accuracy balance. The new detection framework is evaluated on the extensively used KITTI and nuScenes datasets, manifesting the highest inference speed and at the same time outperforming most of the existing single-stage detectors with respect to the average precision. [ABSTRACT FROM AUTHOR]

The safety of vehicles is one of the major goals of driving automation. The safety distance is longer for rail vehicles such as trams because of the adherence limitations of the wheel-to-rail system. The major issues of fixed frontal sensing are fake target detection, blind spots related to rail slopes, curves, and random changes in the target's illumination or reflectivity. In this experimental study, distance measurements were performed using a scaled tram model equipped with a LiDAR sensor with a narrow field of view, under different conditions of illumination, size, and reflectivity of the target objects, and using different track configurations, to evaluate the effectiveness of such sensors in collision-avoidance systems for rail applications. The experimental findings are underlining the sensor's sensitivity to fake targets, objects in the sensor's blind spots, and special optical interferences, which are important for evaluating long-range LiDAR capabilities in sensing safety distance for vehicles. The conclusions can help developers to produce a dedicated colliding prevention system for trams and to identify the zones with high risk in the track where additional protection methods should be used. The LiDAR sensor must be used in conjunction with additional sensors to perform all the security tasks of an anti-colliding system for the tram. [ABSTRACT FROM AUTHOR]

A bending vector sensor based on a ring laser and super-mode interference in a seven-core fiber (SCF) is presented. The super-mode interference is originated via a Mach–Zehnder interferometer constructed with an SCF spliced between two single-mode fibers. The interference modulates the cavity laser losses of an erbium-doped ring cavity laser. As the interferometer structure is bent, the interference pattern suffers a wavelength shift, reconfiguring the cavity losses and tuning the laser emission. When the interferometer structure is bent in different directions, the cavity losses are rearranged differently depending on the direction, resulting in different laser outputs. Relating the laser output emission to the bending position, a bending vector sensor is obtained. The identification of the bending direction is related to the peak intensity, whereas the curvature magnitude to the wavelength peak. • Demonstration of a laser bending sensor based on interference in a seven-core fiber. • Determination of four bending directions with behavior of more than those directions. • Favorable temporal stability with minimal fluctuations in wavelength and intensity. [ABSTRACT FROM AUTHOR]

Wheel is an important component of bogie, and its geometry affects the dynamic behaviors of rail vehicles. To accurately obtain the geometric feature information of wheel tread profile, an on-machine measurement method with the 1-D laser sensor is investigated in this article. Combined with the specific morphology of tread profile and measurement model of laser sensor, the corresponding on-machine measurement model of wheel tread profile is established according to the characteristics of inclined and curved profiles. To improve the measurement accuracy, an adaptive error correction is studied to strengthen the measurement of complex profiles and suppress the measurement error of laser sensors. And the method achieves error suppression by more than 30% in the test and calibration experiments. Finally, the measurement method based on the laser sensor is put into the practical measurement of the wheel tread profile on an underfloor wheelset lathe, and the effectiveness of the proposed method is verified with experiments. These works can not only improve the measurement accuracy of wheel geometry but also enhance the machining capability of machine tool, which is important for the safe operation of rail vehicles. [ABSTRACT FROM AUTHOR]

Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spectroscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments. This study investigates the use of Light Detection and Ranging (LiDAR) and streaming video to enable real-time object detection and tracking, and the fusion of this tracking information with radiological data for the purposes of enhanced situational awareness and increased detection sensitivity. This work presents an object detection, tracking, and novel source–object attribution analysis that is capable of operating in real time. By implementing this analysis pipeline on a custom-developed system that comprises a static 2 in. × 4 in. × 16 in. NaI(Tl) detector colocated with a 64-beam LiDAR and four monocular cameras, we demonstrate the ability to accurately correlate trajectories from tracked objects to spectroscopic gamma-ray data in real time and use physics-based models to reliably discriminate between source-carrying and nonsource-carrying objects. In this work, we describe our approach in detail and present a quantitative performance assessment that characterizes the source–object attribution capabilities of both video and LiDAR. Additionally, we demonstrate the ability to simultaneously track pedestrians and vehicles in a mock urban environment and use this tracking information to improve both detection sensitivity and situational awareness using our contextual-radiological data fusion methodology. [ABSTRACT FROM AUTHOR]

We present a flexible sensor fusion approach to retrieve scale information in monocular visual odometry (VO) through integrating range measurements from a wide variety of depth sensors spanning from small-resolution time-of-flight (ToF) cameras to 2-D and potentially 3-D LiDARs. While many algorithms exist in literature for range-enhanced monocular VO, the majority of them are tailored for a specific sensor choice, limiting the integration on generic mobile systems. Our monocular VO algorithm builds on a standard front end, where the camera tracking is performed relative to a map of triangulated landmarks. The inherent scale ambiguity and drift in monocular perception are resolved by optimizing both the camera poses as well as the landmark map with the depth information provided by the range sensor. Performances have been tested on the custom data sets created with an experimental platform comprising a stereo camera, a low-resolution ToF camera, and a 2-D LiDAR. We present a detailed overview of the extrinsic calibration procedures including an ad hoc solution applicable to very low-resolution depth sensors. The proposed system is tested on short- and long-range motions, showing that: 1) performances in each of the tested configurations are on par or better than the state-of-the-art stereo systems and 2) it is sufficient to use a reduced number of range measurements to obtain a scaled trajectory accurately. [ABSTRACT FROM AUTHOR]

A new method for measuring fat concentration in milk based on a He-Ne laser sensor at room temperature (25°C) is presented. The measurement system consists of signal acquisition module and signal processing module. The signal acquisition module consists of a light source, photodetector, temperature control module and a lifting platform. The signal processing module mainly consists of signal amplification module, signal conversion module and microprocessor module. According to the characteristic of fat in milk, the corresponding laser light source and photodetector are selected. Milk samples with different fat concentration have different levels of reflectance. Based on this, the model of fat concentration and diffuse reflection light intensity in milk is established. The research shows that the fat concentration can be measured accurately by the laser sensor system, and the feasibility of the method is also verified. Meanwhile, the method proposed in this paper can measure the fat concentration in milk in real time, which provides a new way for detecting main component concentration in foods. [ABSTRACT FROM AUTHOR]

• A highly sensitive Airborne Laser Photo-Acoustic CH 4 Gas Analyzer (7.7 μm) is developed. • A resonant differential photo-acoustic detector and a sealed-off gas-filled Ref -cell was used. • The minimum measured background signal of the PA sensor is n min ≈ (26.6 ± 8.4) ppb CH 4 (at a bandwidth of 20 Hz). • The value of normalized noise equivalent absorption is (NNEA) = 8.22 × 10–10 cm−1·W/Hz1/2. A compact laser photo-acoustic (PA) methane sensor based on a quantum-cascade laser (∼7.7 μm; 1750 Hz; 25 mW), a resonant differential photo-acoustic detector (PAD), and a sealed-off gas-filled PA Ref -cell has been developed. Normalization of the absorption signals in the PAD is carried out according to the absorption signals in the gas-filled PA Ref -cell, which significantly reduces the measurement errors of the methane concentration in the case of instability of the laser emission wavelength. The minimum measured background signal of the PA sensor (using high purity nitrogen) is n min ≈ (26.6 ± 8.4) ppb CH 4 (at a bandwidth of 20 Hz), the value of normalized noise equivalent absorption (NNEA) = 8.22 × 10–10 cm−1·W/Hz1/2. A comparison of various research groups results with mid-IR PA gas analyzers is carried out. The developed PA methane sensor is adapted for placement on the UAV's board. Device has dimensions of 315 × 165 × 110 mm, weight ∼3.1 kg, power supply from an external source (9...60 VDC), power consumption ∼20 VA. [ABSTRACT FROM AUTHOR]

• Open-path standoff gas detection for atmospheric NO using WMS-2f/1f method and QCL. • Adaptive Kalman filter improves 33% in detection accuracy with a response time of 7 s. • Dynamic response, accuracy, sensitivity, and detection limit are evaluated in the laboratory. • Temporal evolution of NO in the atmosphere is measured by field experiments. Nitric oxide (NO) is one of the primary air pollutants, and open optical path detection of NO holds immense significance in safeguarding the ecological environment and human health. In this paper, a high precision and high sensitivity NO open optical path sensor based on mid-infrared laser absorption spectroscopy technology is developed. The average gas concentration inversion of the NO path is achieved by collecting the backscattered light from the cooperative target. The gas measurement deploys a wavelength modulation method to remove the effects from light intensity variations. Furthermore, a Kalman filter and a temperature–pressure sensor optimization software algorithm were utilized to reduce the effects of temperature–pressure variations during the measurement process, leading to a 33% improvement in detection accuracy. The performance of the sensor was evaluated in the laboratory environment in terms of accuracy, sensitivity, and detection limit. The atmospheric NO concentration was also measured under varying time periods and weather conditions in the field environment, thus laying a strong foundation for studying the spatial and temporal evolution of NO in the atmosphere. [ABSTRACT FROM AUTHOR]

The flatness error is one of the critical parameters of a circular saw, which seriously affects the cutting performance. In this paper, the measurement of the circular saw flatness error based on a laser displacement sensor is proposed. For the tooth of a circular saw, the plane is discontinuous. According to the field detection demand of a circular saw, the authors collected Archimedean spiral three coordinate data of the circular saw using a laser displacement sensor. Considering the practical application of circular saw flatness detection, the authors have established an ideal plane model of the flatness based on the least‐squares method to evaluate the flatness error of circular saws. The system has a maximum detection area of 400 mm × 400 mm, the measurement time is 80 s, and the measurement absolution accuracy is 10 μm. [ABSTRACT FROM AUTHOR]

A new method of measuring the static deformation of rotating blades based on BTT is proposed and examined in experiments. To achieve high accuracy and robustness, the method utilizes coordinates on both the suction surface and the pressure surface from multiple probes and fits them to the complete tip profile. To realize this, a backward fitting algorithm is designed which solves the deformation parameters in a coupled fashion. Hardware advancement is also implemented where the departure time instants besides the arrival time instants of each blade are collected. A novel validation method using the laser displacement sensor is also presented to provide a benchmark. In the laboratory experiment, it is found that the new method could reduce the calibration uncertainty to 43%–61% by taking the suction surface into account. Compared to the existing methods, the measurement error of the new method is also the smallest. The proposed method has been validated on a single-rotor compressor rig. • A new method (FullProFit) to measure the static deformation of rotating blades. • Utilization of the measured coordinates on the suction surface. • Using backward fitting to incorporate multiple samples and elevate efficiency. • Direct experimental validation based on the laser displacement sensor. [ABSTRACT FROM AUTHOR]

• A high-sensitive multi-pass photoacoustic spectroscopy sensor is developed for hydrogen sulfide. • With the enhancement of effective laser power in the multi-pass PA cell, the 2 f -signal of the target H 2 S line has a ∼ 8 times improvement. • The minimum detection concentration of the system can reach 0.13 ppm with a 1-minute averaging time. A high-sensitive tunable diode laser sensor based on photoacoustic spectroscopy and multi-pass absorption spectroscopy is developed for hydrogen sulfide (H 2 S) detection using a target absorption line at 6287.005 cm−1. Two spherical concave mirrors are utilized as the reflectors to make the laser beam pass through the photoacoustic cell for 24 times. The wavelength modulation spectroscopy technique with second harmonic detection (WMS- 2f) method is employed to reduce external interference and increase the detection sensitivity. With the enhancement of the effective laser power in the multi-pass PA cell, the photoacoustic 2f signal of the target H 2 S absorption line has a ∼ 8 times improvement compared to the sensor system without multi-pass cell. Based on the results of Allan variance analysis, the minimum detection concentration of the system could reach 0.68 ppm with a 10-s average time and it can be further lowered down to ∼ 0.1 ppm when a 1-minute averaging time is used. The linear responsivity of the multi-pass PA spectroscopy sensor is estimated to be ∼ 0.00112 mV/ppm by linear fitting for a continuous flow gas measurement results. The excellent performance validates the potential of the sensor for field application. [ABSTRACT FROM AUTHOR]

When using single-photon detector array lidar to detect ships in a foggy environment, the multiple scattering between laser and sea fog, sea surface and ships leads to a large number of fake images in the reconstructed depth images, which seriously limits the ability to detect and recognize ships. Here, we propose a three-dimensional imaging algorithm suitable for ships in fog with a single-photon detector array lidar. This algorithm can eliminate fake images caused by multiple reflections of the sea surface in a foggy environment. Firstly, the algorithm uses a dual-Gamma estimation algorithm to eliminate the influence of non-signal photons, and uses a pulse-like selection algorithm to accurately extract the target echo waveform. Secondly, based on the characteristics of the target echo, the principal component analysis (PCA) and K-means algorithm are used to cluster the features of the separated echo signals. Finally, the result of feature classification is used to achieve efficient fake images removal. We show that the proposed algorithm can more accurately reconstruct the depth image of a ship with a distance of 1.72 km in fog environment. Compared with the traditional algorithms, the proposed algorithm can suppress the sea surface reflection while retaining the most target contour. It has been successfully demonstrated for small ships at different distances. This research is expected to improve the detection and recognition ability of single-photon detector array lidar for small targets in complex scenes. [ABSTRACT FROM AUTHOR]

In article number 2100511, Nanxi Li and colleagues present a comprehensive review on recent advances in solid-state LiDAR and nanophotonics-based LiDAR sensors. B Light Detection and Ranging (LiDAR) Sensors b Light detection and ranging (LiDAR) sensors enable precision sensing of an object in 3D. Also, LiDAR sensor principles are introduced, and future outlook on advanced LiDAR sensors is discussed. [Extracted from the article]

Foot-mounted inertial sensors can be used for indoor pedestrian navigation. In this paper, to improve the accuracy of pedestrian location, we propose a method using a distance sensor (LIDAR) in addition to an inertial measurement unit (IMU). The distance sensor is a time of flight range finder with 30 m measurement range (at 33.33 Hz). Using a distance sensor, walls on corridors are automatically detected. The detected walls are used to correct the heading of the pedestrian path. Through experiments, it is shown that the accuracy of the heading is significantly improved using the proposed algorithm. Furthermore, the system is shown to work robustly in indoor environments with many doors and passing people. [ABSTRACT FROM AUTHOR]

We study the nonlinear dynamics of a quantum cascade laser (QCL) with a strong reinjection provided by the feedback from two external targets in a double cavity configuration. The nonlinear coupling of interferometric signals from the two targets allows us to propose a displacement sensor with nanometric resolution. The system exploits the ultra-stability of QCLs in self-mixing configuration to access the intrinsic nonlinearity of the laser, described by the Lang-Kobayashi model, and it relies on a stroboscopic-like effect in the voltage signal registered at the QCL terminals that relates the "slow" target motion to the "fast" target one. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the theory and method of gear measurement, the effectiveness of gear contact measurement is reduced, making it difficult to meet the need for 100% gear detection. To improve the measurement efficiency and make effective use of the full tooth surface data, a gear pitch rapid measurement method by point laser sensor is proposed. This method studies the principle of measuring the gear based on a laser point sensor, and the efficient model of collection and analysis of tooth surface data is established. Then the evaluation of the pitch deviation is completed, including the individual single pitch deviation, the single pitch deviation, the individual cumulative pitch deviation, and the total cumulative pitch deviation. Additionally, the pitch deviation of the multiple cross-sections is investigated, which may characterize the overall pitch deviation of the tooth surface. Compared to the gear measuring center P26, the experimental results show that the optical measurement of pitch deviation is feasible and efficient. Depending on the different measuring conditions, the offset status of the sensor can be selected to meet the different efficiency and accuracy requirements. The research and application potential of a point laser sensor in gear measurement is wide-ranging. [ABSTRACT FROM AUTHOR]

An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest Leaf Area Index (LAI) from satellite sensors. The scanning lidar instrument was developed primarily from low cost, commercially accessible components. While the 635 nm wavelength lidar is not ideally suited to vegetation studies, there was an acceptable trade-off between cost and performance. Tests demonstrated reliable range estimates to live foliage up to a distance of 60 m during night-time operation. Given the instrument's scan angle of 57.5 degrees zenith, the instrument is an effective tool for monitoring LAI in forest canopies up to a height of 30 m. An 18 month field trial of three co-located instruments showed consistent seasonal trends and mean LAI of between 1.32 to 1.56 and a temporal LAI variation of 8 to 17% relative to the mean. [ABSTRACT FROM AUTHOR]

Mapping the environment is necessary for navigation, planning and manipulation. In this paper, a fusion framework (as data-in-decision-out) is introduced for a 2D LIDAR and a 3D ultrasonic sensor to achieve three-dimensional mapping without expensive 3D LiDAR scanner or visual processing. Two sensor models are proposed for the two sensors used for map updating. Furthermore, 2D/3D map representations are discussed for our fusion approach. We also compare different probabilistic fusion methods and discuss criterias for choosing appropriate methods. Experiments are carried out with a real ground robot platform in an indoor environment. The 2D and 3D map results demonstrate that our approach is able to show the surrounding in more details. Sensor fusion provides a better estimation of the environment and the ego-pose whilst lowering the necessary resources. This gives the robot's perception of the environment more information by using only one additional low-cost 3D ultrasonic sensor. This is especially important for robust and light-weight robots with limited resources. [ABSTRACT FROM AUTHOR]

LiDAR matching between real-time point clouds and pre-built points map is a popular approach to provide accurate localisation service for autonomous vehicles. However, the performance is severely deteriorated in dense traffic scenes. Unavoidably, dynamic vehicles introduce additional uncertainty to the matching result. The main cause is that the pre-built map can be blocked by the surrounding dynamic vehicles from the view of LiDAR of ego vehicle. A novel uncertainty of LiDAR matching (ULM) estimation method aided by the dynamic vehicle (DV) detection and high definition map is proposed in this Letter. Compared to the conventional Hessian matrix-based ULM estimation approach, the proposed method innovatively estimates the ULM by modelling surrounding DV. Then the authors propose to correlate the ULM with the detected DV and convergence feature of matching algorithm. From the evaluated real-data in an intersection area with dense traffic, the proposed method has exhibited the feasibility of estimating the ULM accurately. [ABSTRACT FROM AUTHOR]

A fiber laser curvature sensor based on dual-wavelength gain competitive intensity difference demodulation is proposed and experimentally demonstrated. Here a linear cavity dual-wavelength erbium-doped fiber laser (DWEDFL) is build with two filter components i.e. fiber Bragg gratings (FBG) with very close center wavelengths. Since the dual wavelengths are sharing the same gain medium erbium-doped fiber (EDF), gain competition mechanism is formed with sensitive feature to external measurand curvature. Besides, the curvature information is demodulated by subtracting the power of the dual wavelengths. Experimental results show a total sensitivity as high as -57.22 dB/m−1. Our scheme not only improves the measuring sensitivity, but also eliminates the effects of source power jitter and external disturbances for example temperature shift since they do almost the same impact on the dual wavelengths. [ABSTRACT FROM AUTHOR]

Schematic representation of the proposed sensing system based on laser-scribed graphene modified molecularly imprinted polymer for Bisphenol A. • Simple, mask-free, low-cost sensor based on LSG technology combined to MIPs for BPA determination. • Fabrication of LSG electrodes by CO 2 laser with low resistivity 58 Ω/square on flexible polyimide sheet. • Sensitive electrochemical detection of BPA with a LOD of 8 nM. • Accurate detection of BPA in tap, mineral water and in plastic samples. Bisphenol A (BPA) is a toxic chemical used by industries for production of containers for storage of food and beverages leading to possible health risks. In this work, we present a simple, mask-free, low-cost imprinted sensor based on laser scribed graphene (LSG) technology combined to molecularly imprinted polymers (MIPs) for BPA determination. CO 2 laser was used in production of LSG electrodes with high conductivity and multilayer structure by using less laser speed/ power (2.8 cm/s /3.2 W) and low resistivity 58 Ω/square on flexible polyimide sheet leading to the high active surface area of the sensor. LSG device was functionalized with imprinted polypyrrole with a known amount of BPA as template molecule to develop the sensor. The bare LSG, LSG-MIP, LSG-NIP sensors were characterized using Raman spectroscopy, SEM, XRD, AFM. The electrochemical measurements were carried out using cyclic voltammetry and differential pulse voltammetry. Experimental conditions were optimized, including the concentration of pyrrole monomer, the number of polymerization cycles, the concentration of BPA as template and the incubation time. We evaluated the sensitivity of the LSG-MIP sensor in the concentration range between 0.05 μM and 20 μM with a limit of detection of 8 nM. The proposed sensor exhibits high selectivity towards BPA compared to its structural analogs and good reusability. The developed sensor was successfully applied for the detection of BPA in tap, mineral water and in plastic samples. The developed sensor was integrated into a PMMA case connected to the potentiostat to achieve complete isolation and a practical measurement system. [ABSTRACT FROM AUTHOR]

A relative humidity (RH) sensor based on femtosecond laser induced spiral micro-structured single mode-multimode-single mode (FISM-SMS) fiber structure was experimentally demonstrated. The spiral micro-groove ablated multimode fiber of the FISM-SMS fiber structure was coated with RH sensitive polyvinyl alcohol (PVA) whose refractive index varies as a function of ambient humidity level. Due to the FISM-SMS fiber structure highly sensitive to ambient refractive index, transmission spectrum of the RH sensor changed greatly at different ambient relative humidity. RH response of the sensor was tested by putting it into a homemade climatic chamber whose humidity level can be adjusted by a series of saturated salt solutions. Experimental results show that a RH sensitivity of ∼650 pm/%RH with linearity of ∼0.99 when humidity level varies from 35 % RH to 70 % RH. Besides, reversibility and stability make the sensor a good candidate for RH sensing. [ABSTRACT FROM AUTHOR]