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Polymer-derived ceramics (PDCs) are increasingly recognized as promising materials for high-temperature thin/thick film sensors (HTTFSs). However, a large linewidth and poor dimensional accuracy have severely restricted the practical application of PDCs HTTFSs. Our study employed a picosecond laser to precisely pattern the polymer-derived ceramic (PDC) thick film while applying no obvious damage to the interface between ceramic and substrate. Through parameters optimization, the laser-affected area was minimized to 6 μm wide, and a minimum feature size of about 30 μm was achieved. Furthermore, 7 rounds of high-temperature tests of the patterned film revealed good temperature-resistance repeatability within a temperature range of up to 800 °C. To demonstrate practical application potential, we successfully fabricated a heat flux sensor, and it exhibited a sensitivity of 1.32 mV/(kW/m2), a response time of 600 ms, and excellent repeatability over a range of 0–75.4 kW/m2. In summary, this paper proposes a novel approach toward miniaturizing the linewidth of the polymer-derived ceramics thick film sensor, showing significant potential for precise measurements in extreme environments. [Display omitted] [ABSTRACT FROM AUTHOR]

Multimodal perception represents a vital approach for observing non-cooperative targets in space. The accurate determination of the extrinsic parameters for monocular camera and LiDAR systems serves as the cornerstone for achieving effective space multimodal perception. However, due to factors like launch and operation, these extrinsic parameters can experience drift, thereby compromising the overall performance of the multimodal perception system. To tackle this challenge, this study introduces a novel approach: an edge feature-based external calibration method for monocular camera and LiDAR using in-orbit data. The proposed methodology involves several steps. Firstly, edges are extracted from the image, followed by distance transformation based on the edge image. The subsequent integration of the edge image and distance image yields the search scene image. Then, edge feature is extracted from the point cloud data. Finally, after constructing the objective function, genetic algorithms are used to determine precise external parameters. Simulation results underscore the effectiveness of the proposed algorithm, affirming its ability to achieve high-precision calibration results for external parameters even within intricate spatial environments. [ABSTRACT FROM AUTHOR]

Optical remote sensing techniques can indicate the properties of objects by observing different modalities (physical quantities) of the backscattered light at different optical wavelengths. Established examples are reflectance, fluorescence, Raman, or depolarization spectroscopy. LiDAR sensing, on the other hand, allows acquiring the geometry of objects by measuring the propagation delay of optical probing signals. Multimodal multispectral (MM) LiDAR combines these capabilities and extends conventional monochromatic LiDAR in both spectral and modal dimensions within a single instrument, thus enriching point cloud data with non-geometric information. The potentially high dimension of MM LiDAR data, however, poses significant challenges for instrumental design, data acquisition, and data processing. MM LiDAR data are structured as several or all modalities are available in each of the spectral channels. The above challenges can thus be mitigated by feature selection (FS), if the structure of the features is taken into account, i.e., if entire spectral channels or entire modalities are selected or omitted. Herein, we focus on the feature selection method for MM LiDAR and propose a multiclass group feature selection algorithm (MGSVM FS) consisting of a structural sparsity-based embedded feature selection method with an all-in-one support vector machine (SVM). It tackles jointly the challenges arising from the high dimension of the MM data and the need for a multiclass classification task while exploiting the structure of the MM data. In addition, we introduce a complete workflow for evaluating the feature selection and for decision-making. We apply the framework to selecting an optimum spectral and modal configuration for remote material classification using an experimental MM LiDAR system that provides reflectance, distance, and degree of linear polarization in 28 spectral channels of 10 nm width. For the experimental investigation, we use MM LiDAR data obtained in a controlled lab environment from thirty specimens of four material classes relevant for construction. Using all three modalities, we find a configuration with only 3 spectral channels that achieves a classification mean-F1 score of 100% within this small dataset. Similar classification performance can also be achieved with only two modalities when using more spectral channels. MGSVM FS improves the classification mean-F1 score by up to 25% as compared to random selection and outperforms two other commonly used filter and embedded feature selection methods, in this application example. The proposed group feature selection algorithm and decision-making are useful for MM LiDAR, providing a link between instrumental design, data acquisition, and data processing. However, they are also transferable to other application fields related to multiclass classification, regression, and knowledge discovery, with features structured in groups. The collected MM feature dataset, the MGSVM FS algorithm, and the evaluation pipeline are accessible online. 1 1 https://github.com/yuhan-yhyh/Dataset_Code_MGSVM-FS-MM-LiDAR.git. [ABSTRACT FROM AUTHOR]

Multi-camera systems for structure from motion (SfM) are widely deployed in many mapping applications. Existing solutions assume known rig calibration, synchronized frames among cameras, as well as overlapping field of views (FoVs). In this paper, we derive novel geometric constraints assuming minimal knowns about the multi-camera systems, to benefit low-cost and non-expert use cases where uncalibrated multi-camera systems with non-typical geometry setups present, i.e., no rig calibration, no overlapping FoVs. Assuming that these cameras are co-located and share the same motion of the platform, the proposed constraints utilize the parallelism and length proportionality of motion vectors of these co-located cameras and formulate them as translation constraints into the bundle adjustment (BA). The proposed constraints (called motion constraints) impose a first-order penalty to co-located cameras whose motion speeds and directions between frames do not match. With soft constraints, it can handle loosely synchronized frames (with an error within one second). The proposed constraints are integrated into the BA framework and experimented with different camera setups, i.e., on a group of casually co-located GoPro cameras with no rig calibration, and some with no overlapping views. Our results show that the constraints are extremely effective in improving the reconstruction and pose accuracy for ground motion images: in our self-collected open trajectories without loop closure, the proposed constraints are effective in correcting topographical errors (i.e., trajectory drifts) of the resulting models, and the dense point clouds achieve up to 11.34 m (86.12 %) of mean absolute error (MAE) improvement as compared to reference LiDAR point clouds; our results on KITTI-odometry and KITTI-360 datasets also show an improvement of up to 28.82 m (81.05 %) in terms of the root mean square error (RMSE) of absolute pose error (APE). We expect that the proposed constraints are significant not only as additional geometric constraints for image-based mobile mapping, but also will benefit the broader use of photogrammetry, since it empowers the possibility to harness the traditionally so-called low-quality stereo/multi-camera data (e.g., by non-photogrammetry citizen scientists) into improved 3D products. [ABSTRACT FROM AUTHOR]

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally segmenting the pre-processed global navigation satellite system/inertial navigation system solution and representing LiDAR data as voxels with metadata. The efficiency, accuracy, and scalability of different voxel sizes have been assessed in this study. Hybrid adjustment of image tie-points and voxels from LiDAR data optimized the trajectory and sensor corrections for both LiDAR scanners and cameras in the presented experiment. A little over one billion LiDAR points and ∼ 3000 images were matched in < 1. 5 h and showed high accuracy in the range of a few millimeters. This accuracy is comparable to the existing methods for hybrid image and LiDAR adjustment. [ABSTRACT FROM AUTHOR]

Accurate camera localization in existing LiDAR maps is promising since it potentially allows exploiting strengths of both LiDAR-based and camera-based methods. However, effective methods that robustly address appearance and modality differences for 2D–3D localization are still missing. To overcome these problems, we propose the I2D-Loc, a scene-agnostic and end-to-end trainable neural network that estimates the 6-DoF pose from an RGB image to an existing LiDAR map with local optimization on an initial pose. Specifically, we first project the LiDAR map to the image plane according to a rough initial pose and utilize a depth completion algorithm to generate a dense depth image. We further design a confidence map to weight the features extracted from the dense depth to get a more reliable depth representation. Then, we propose to utilize a neural network to estimate the correspondence flow between depth and RGB images. Finally, we utilize the BPnP algorithm to estimate the 6-DoF pose, calculating the gradients of pose error and optimizing the front-end network parameters. Moreover, by decoupling the intrinsic camera parameters out of the end-to-end training process, I2D-Loc can be generalized to the images with different intrinsic parameters. Experiments on KITTI, Argoverse, and Lyft5 datasets demonstrate that the I2D-Loc can achieve centimeter localization performance. The source code, dataset, trained models, and demo videos are released at https://levenberg.github.io/I2D-Loc/. [ABSTRACT FROM AUTHOR]

Laser heterodyne radiometer (LHR) exploits the heterodyne signal generated by solar radiation beated with local oscillator (LO) light to extract information on atmospheric species in the atmospheric column. Most of currently reported LHRs are affected by LO-induced relative intensity noise (RIN), which poses a challenge for high-sensitivity detection. In this work, a near-infrared RIN-suppressed LHR is proposed for simultaneous detection of water vapor and HDO in the atmospheric column. The operability of a home-made sun tracker can be obtained from coarse tracking and fine tracking, which offers an excellent opportunity for unattended observation. A tunable distributed feedback (DFB) diode laser centered at 1552.9 nm is employed as LO, where LO-induced RIN is suppressed by a semiconductor optical amplifier (SOA) operating in the dynamic gain saturation regime. By locking LO power and suppressing LO-induced RIN, the SOA-assisted LHR is dominated by 1.27 times shot-noise and the signal-to-ratio (SNR) of heterodyne signal is increased by 5 times. Based on atmospheric transmission spectra measured with the RIN-suppressed LHR in Hefei, China, the total column abundances of water vapor and HDO are retrieved to be 2050 ppm and 0.6 ppm, respectively, with uncertainty of 2 %. • A high-sensitivity H 2 O/HDO sensor based on laser heterodyne spectroscopy. • A coarse and fine tracking sun tracker was designed for unattended observation. • Laser power optimization and RIN suppression were proposed to reduce LHR noise. • The LHR achieved an operation in an almost shot-noise dominated regime. [ABSTRACT FROM AUTHOR]

The complex and dynamic human environment, higher requirements are put forward for the high-performance flexible pressure sensors. The materials selection and microstructure design are crucial to achieve high sensitivity and stability. In this work, we fabricated pressure sensitive layer with multiple dimensional materials. Using laser ablation in liquid method, Ag nanoparticles were uniformly distributed in surface of multi-walled carbon nanotubes (MWCNT). Combined with the delaminated MXene, highly pressure sensitive composite could be prepared due to the reduction of contact resistance under pressure. The corresponding piezoresistive sensors showed high sensitivity (12.7 kPa−1), excellent response speed (150 ms), recovery speed (100 ms), and distinguished cycle stability (3000 cycles). Based on the performance of the sensor, it can realize the monitoring of tiny pressure on the human body, and further realize the detection of human activities and health. In addition, the sensor array has spatial recognition capability, providing a way of thinking for the development needs of future intelligent wearable electronic devices. The sensors can measure changes in resistance caused by finger bending, slight breathing, pulse, vocal cord vibration, and other movements to realize human activity and health monitoring. At the same time, the 3×3 sensor array system can recognize the layout of pressure in space. This simple preparation and low cost of the sensor can Facilitate the progress towards sophisticated wearable electronic gadgets. [Display omitted] • The sensor with Ag-MWCNT/MXene as sensing layers had high sensitivity (12.7 kPa−1), excellent response speed (150 ms), recovery speed (100 ms), and distinguished cycle stability (3000 cycles). • The sensors can measure changes in resistance caused by finger bending, slight breathing, pulse, vocal cord vibration, and other movements to monitor human activity and health. • The fabricated sensor can recognize the layout of pressure in space to explore its sensing ability for pressure distribution and its application in the field of artificial electronic skin. [ABSTRACT FROM AUTHOR]

• A generalized theoretical model (GTM), suitable for various imaging-based atmospheric lidar techniques, has been concepted and established. • The dependencies of the range resolution, the blind range, etc. on optical configurations have been revealed based on the GTM and simulation studies. • A number of factors, providing significant guidance for quantitative system designment, have been proposed and simulated. In recent years, imaging-based lidar techniques have been widely studied and employed in atmospheric remote sensing, while different theoretical descriptions are introduced for lidar systems with specific optical configurations and a universal guideline for optimizing system performance is still lacking. In this work, a generalized theoretical model (GTM), suitable for various imaging-based atmospheric lidar techniques, has been concepted and established for describing the relationship between the system performances and optical configurations, where a few factors have been derived and defined. Comprehensive simulation studies based on the GTM have demonstrated that the range resolution, the blind range, the minimum scattering angle of the lidar signal and the tilt angle of the image sensor are strongly dependent on the focal length of the receiving lens (or telescope) and the transmitter–receiver separation, etc. In particular, the range resolution is proportional to the product between the focal length and the separation. Besides, the intensity factor, the signal-to-background ratio (SBR) factor, as well as the signal-to-noise ratio (SNR) factor, which are immune from atmospheric conditions, etc., have been proposed and deduced to quantify the performances of imaging-based lidar system with different optical configurations. It has been found out that the SBR factor, an indicator for the requirement on the dynamic range of the image sensor, is inversely proportional to the separation. Meanwhile, the intensity factor and the SNR factor are related to the separation, the focal length as well as the aperture of the receiving lens, etc. The GTM provides valuable insight for understanding the principle of the imaging-based atmospheric lidar technique as well as a general guideline for system design and implementation. [ABSTRACT FROM AUTHOR]

• In this study, we first report the Q-switched temperature fiber laser sensing device. The large core multimode fiber - graded index multimode fiber is used as the saturable absorber to generate pulsed laser, and the D-shaped multimode fiber is used as the sensing unit for temperature detection. For your reference, a brief summary of the novelty of our work is provided below. • Strong Nonlinear Optical Response. The prepared saturable absorber had excellent linear and nonlinear capability at 1560nm. • Excellent nonlinear optical properties. The Q-switched laser with a central wavelength of 1561nm was realized. • A high-performance Q-switched temperature sensing device. Temperature Q-switched sensing can be achieved. In the paper, a Q-switched temperature sensing device is proposed and verified, which consists of a modulator based on a large core multimode fiber and a graded index multimode optical fiber (LMF-GIMF), and a sensing unit formed by sandwiching a D-shaped multimode fiber (MMF) between two single mode fibers (SMF). Utilizing the modulator to transform continuous light into pulsed laser, a stable Er3+-doped Q-switched laser with a repetition frequency of 23.04 kHz and a modulation depth of 7.47 % has been achieved. Under its pumping, the SMF-MMF-SMF sensing structure performed thermal detection with a sensitivity of −81.5 pm/℃ as temperature varies in the range of 25 °C to 65 °C. The temperature sensing device based on Q-switching can be used as an independent pulse laser to realize the switch between the light source and the sensing device. At the same time, the sensing device can provide real-time feedback according to the change of the central wavelength, which allows for immediate adjustments in the operational state, enhancing the flexibility and responsiveness. In addition, the characteristics of the sensor based on Q-switched laser can reduce thermal damage and extends the service life of the sensor. As far as we know, this is the first report on Q-switched temperature sensing based on the LMF-GIMF modulator. The proposed sensing device has the advantages of high signal-to-noise ratio, low operating threshold, minimal thermal damage, and serves as an independent Q-switched laser source, which can be applied to pulse laser measurement with high precision and time resolution in the future. [ABSTRACT FROM AUTHOR]

Multi-modal remote sensing (RS) involves the fusion of data from multiple sensors, such as RGB, Multispectral, Hyperspectral, Light Detection and Ranging, Synthetic Aperture Radar, etc., each capturing unique information across different regions of the electromagnetic spectrum. The fusion of different modalities can provide complementary information, allowing for a comprehensive understanding of the Earth's surface. Multi-modal RS image segmentation leverages various RS modalities to achieve pixel-level semantics classification. While deep learning has demonstrated promise in this domain, the limited availability of labeled multi-modal data poses a constraint on leveraging data-intensive techniques like deep learning to their full potential. To address this gap, we present Ticino, a novel multi-modal remote sensing dataset tailored for semantic segmentation. Ticino includes five modalities, including RGB, Digital Terrain Model, Panchromatic, and Hyperspectral images within the visual-near and short-wave infrared spectrum. Specifically annotated for Land Cover and Soil Agricultural Use, the dataset serves as a valuable resource for researchers in the field. Additionally, we conduct a comparative analysis, comparing single-modality with multi-modality deep learning techniques and evaluating the effectiveness of early fusion versus middle fusion approaches. This work aims to facilitate future research efforts in the domain by providing a robust benchmark dataset and insights into the effectiveness of various segmentation approaches. • A multi-modal Remote Sensing (RS) dataset for semantic segmentation. • The dataset includes RGB, Panchromatic, Hyperspectral, and Digital Terrain Model. • The dataset includes labeling for Land Cover and Soil Agricultural Use. • A comparison of single- vs multi-modality and early vs middle fusion DL techniques. • A validation of the multi-modality superiority in RS semantic segmentation. [ABSTRACT FROM AUTHOR]

With the objective of improving the registration of lidar point clouds produced by kinematic scanning systems, we propose a novel trajectory adjustment procedure that leverages on the automated extraction of selected reliable 3D point–to–point correspondences between overlapping point clouds and their joint integration (adjustment) together with raw inertial and GNSS observations. This is performed in a tightly coupled fashion using a dynamic network approach that results in an optimally compensated trajectory through modeling of errors at the sensor, rather than the trajectory, level. The 3D correspondences are formulated as static conditions within the dynamic network and the registered point cloud is generated with significantly higher accuracy based on the corrected trajectory and possibly other parameters determined within the adjustment. We first describe the method for selecting correspondences and how they are inserted into the dynamic network via new observation model while providing an open-source implementation of the solver employed in this work. We then describe the experiments conducted to evaluate the performance of the proposed framework in practical airborne laser scanning scenarios with low-cost MEMS inertial sensors. In the conducted experiments, the method proposed to establish 3D correspondences is effective in determining point–to–point matches across a wide range of geometries such as trees, buildings and cars. Our results demonstrate that the method improves the point cloud registration accuracy (∼ 5 × in nominal and ∼ 10 × in emulated GNSS outage conditions within the studied cases), which is otherwise strongly affected by errors in the determined platform attitude or position, and possibly determine unknown boresight angles. The proposed methods remain effective even if only a fraction (∼ 0.1%) of the total number of established 3D correspondences are considered in the adjustment. [ABSTRACT FROM AUTHOR]

Co-registration of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, and SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. First, to address severe NRD, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed, which combines the first- and second-order gradient information by using the steerable filters with a multi-scale strategy to depict more discriminative structure features of images. Then, a fast similarity measure is established called Fast Normalized Cross-Correlation (Fast-NCC SFOC), which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. Furthermore, to achieve reliable registration performance, a coarse-to-fine multimodal registration system is designed consisting of two pivotal modules. The local coarse registration is first conducted by involving both detection of interest points (IPs) and local geometric correction, which effectively utilizes the prior georeferencing information of RS images to address global geometric distortions. In the fine registration stage, the proposed SFOC is used to resist significant NRD, and to detect control points (CPs) between multimodal images by a template matching scheme. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images. The results show its superior matching performance compared with the state-of-the-art methods. Moreover, the designed registration system also outperforms the popular commercial software (e.g., ENVI, ERDAS, and PCI) in both registration accuracy and computational efficiency. Our system is available at https://github.com/yeyuanxin110/SFOC-Multimodal_Remote_Sensing_Image_Registration_System. [ABSTRACT FROM AUTHOR]

Accurate pose estimation of monocular ground-level images with respect to a satellite/aerial photogrammetric dataset is an extremely challenging task. Existing solutions often perform an offline post-registration on 3D results from both sources, which however, suffers from non-rigid geometric distortions of the 3D monocular reconstruction and the lack of overlaps between the air to ground content. This paper provides an online solution that performs accurate pose estimation of the ground images with respect to a 3D model derived from satellite images, followed by a dense 3D reconstruction. Our solution takes advantage of the simultaneous localization and mapping (SLAM) paradigm to dynamically incorporate reference observations from the satellite 3D model during the incremental pose estimation, called a cross-view SLAM solver, which leverages both ground-to-satellite error and image-level reprojection errors at the frame level to yield image poses that are well-registered to the satellite 3D model for facade point cloud reconstruction. This process also has the advantage of correcting non-rigid distortions and trajectory drifts that are often presented in monocular SLAM systems. In addition, our solution leverages both the geometric and semantic information from the satellite model and ground images to perform a per-frame correction for frame-level pose initialization, in which a novel scheme called pose buffer is introduced to initialize the pose of each keyframe through robust visual hull alignment of ground objects. The proposed approach has been experimented using four trajectories of monocular videos collections (around 7,000 frames per trajectory on average) and a 3D semantic model from multi-view satellite images to estimate the poses of the video frames and yield point clouds consistent with the satellite 3D models, evaluated by using LiDAR ground-truth. Both qualitative and quantitative experiments demonstrate that our solution yields accurate, drift-free poses and point clouds consistent with the satellite data and visually much more pleasing 3D models with facade information. Compared to the LiDAR ground-truth, the derived 3D models with ground-level images have achieved a mean absolute error of 1.78 m (improved from 3.15 m achieved using SLAM without utilizing satellite 3D models) (A testing program will be made available through https://github.com/GDAOSU/Cross-View-SLAM). [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]

Handling and interpreting sparse 3D point clouds, especially from mmWave radar, presents unique challenges due to the inherent data sparsity and the vast domain difference compared to denser point clouds like those from LiDAR. In this paper, we introduce a novel cascaded generative adversarial network (GAN) approach to bridge this domain gap. The core principle is to progressively refine the radar-based point cloud through a series of GANs, each targeting a higher resolution. By leveraging multi-level features and a hybrid loss function that combines adversarial, geometric, and consistency components, our method ensures a smooth transition from the sparse radar representation to a high-resolution LiDAR-like point cloud. Our cascaded approach operates at a patch level, and the integrated loss function ensures that the generated points not only resemble the target domain but also maintain geometric and structural fidelity. Real-life dataset consisting mostly of moving pedestrians were collected using a system made of Radar, LiDAR, and RGB Camera. Through an extensive experiment on the collected real-world pedestrian dataset, we validate the efficacy of our approach. Inference from the network indicates that our method can upsample mmWave radar point clouds with enhanced density, uniformity, and closer alignment to the ground truth LiDAR point clouds, which is the first of its kind network to do so. • Novel radar point cloud upsampling method using adversarial cascade approach. • Modification of CycleGAN for handling point clouds. • Dynamic batching to handle varying point clouds. • Enhanced radar-based point cloud method for improved people detection. [ABSTRACT FROM AUTHOR]

LiDAR and camera are two common vision sensors used in the real world, producing complementary point cloud and image data. While multimodal data has previously been found mostly in 3D detection and tracking, we aim to study large-scale semantic segmentation by multimodal data fusion rather than only knowledge transfer or distillation. We show that fusing LiDAR features with camera features and abandoning the strict point-to-pixel hard correlation can lead to better performance. Even so, it is still difficult to make full use of multimodal data due to the spatiotemporal misalignment of sensors and uneven data distribution.. To address this issue, we propose the Joint Semantic Segmentation (JoSS), a powerful LiDAR-camera fusion solution that employs the attention mechanism to explore the potential relationships between point clouds and images. Specifically, JoSS consists of commonly used 3D and 2D backbones, and lightweight transformer decoders based on point clouds and images. A point cloud decoder adopts queries to analyze the semantics from LiDAR features, and an image decoder adaptively fuses these queries with corresponding image features. Both exploit contextual information, thus fully mining multimodal information for semantic segmentation. In addition, we propose an effective unimodal data augmentation (UDA) method that performs cross-modal contrastive learning on point clouds and images to significantly improve accuracy by augmenting the point cloud alone without the complexity of generating paired samples of both modalities. Our Joss achieves advanced results in two widely used large-scale benchmarks, i.e. SemanticKITTI and nuScenes-lidarseg. • We revisit the key factor of LiDAR-camera fusion, namely the soft joint mechanism. • We develop an attention-based multimodal fusion in point cloud segmentation. • We build multi-scale pairwise inputs and interact using the dual-stream transformer. • We propose unimodal data augmentation and cross-modal contrastive learning. [ABSTRACT FROM AUTHOR]

• Set up a non-uniform Lidar system based on non-uniform sampling strategy. • Design non-uniform image object detection model with mAP50 of 69.4%. • System imaging speed increased by 66.57%. LiDAR (Light Laser Detection and Ranging) is a potential technology that can acquire multi-dimensional and multi-wavelength imaging information. A non-uniform sampling strategy is implemented to enhance the imaging speed, but this system's data can't be efficiently processed and detected. In this paper, we introduce a novel method for object detection in non-uniform imaging, aiming to improve scene analysis and enhance the object detection effect. We use the KITTI dataset with a non-uniform sampling strategy to generate a simulation dataset and design and train NU-YOLO (Non-uniform YOLO) to detect the object in non-uniform images. We build a non-uniform LiDAR system to verify the object detection performance in real scenarios and the speed boost of the LiDAR system. The simulation and experimental results show that NU-YOLO can detect objects in non-uniform images with a higher mAP50 of 7.4% than YOLO (You Only Look Once) v8. By using a non-uniform sampling strategy, NU-YOLO performs 3% better on the mAP50 metric than the model trained using a uniform sampling dataset with the same counts of sampling points. Notably, the non-uniform imaging system increases in speed compared to its uniform sampling counterpart without compromising imaging quality in critical areas. This work opens new application prospects for more intelligent in LiDAR. [ABSTRACT FROM AUTHOR]

• In this manuscript, we proposed a high-speed spectrum demodulation method with a large dynamic range for fiber-optic Fabry–Perot (F-P) sensor based on scanning laser. • The current was scanned step by step to obtain the wavelength lookup table. Experimental results show that the wavelength tuning range is 1546–1553 nm. 256 equally spaced wave numbers were selected from the obtained wavelength lookup table. By quickly cyclically setting the grating current and phase current of the DBR laser, the laser wavelength is scanned with a repetition frequency of 2 kHz. • Experimental results show that the cavity length demodulation resolution reaches 0.15 nm and the demodulation system has better dynamic response. • Benefiting from the high spectral detection resolution, a large demodulation range is achieved to be 0.3–13 mm. Compared with the spectrum module based demodulation system, this solution significantly reduces the cost, simplifies the algorithm and improves the dynamic range. A high-speed spectrum demodulation method with a large dynamic range for fiber-optic Fabry–Perot sensor is presented. The demodulation system only consists of a near-infrared distributed Bragg reflector (DBR) laser, a fiber circulator, a photodetector and a signal processing circuit. By quickly cyclically setting the grating current and phase current of the DBR laser, the laser wavelength is scanned with a repetition frequency of 2 kHz. The signal processing circuit controls the current of the laser and performs spectrum demodulation simultaneously. Spectral demodulation mainly includes normalization of interference spectrum, fast Fourier transform, phase estimation and cavity length calculation. The displacement stage and piezoelectric ceramics were used to remove the static cavity length and dynamic cavity length of the Fabry–Perot cavity. Experimental results show that the demodulation resolution reaches 0.15 nm. Benefiting from the high spectral detection resolution, a large demodulation range is achieved to be 0.3–13 mm. Compared with the demodulation systems based on spectrum modules, this solution significantly reduces the cost, simplifies the algorithm and improves the dynamic range. [ABSTRACT FROM AUTHOR]

[Display omitted] • Air pollutants' concentration inside an Italian Marian Sanctuary was fully assessed. • Indoor levels of SO 2 , NO 2 and PM10 are of concern for the preservation of artworks. • The indoor/outdoor ratio suggests specific indoor sources for NO 2 , benzene and PM10. • The new axial sampler developed for NO 2 results to be inexpensive, easy to use and reliable. In the present study the concentration of some air pollutants (nitrogen dioxide, sulphur dioxide, hydrogen sulphide, ammonia and BTEX, i.e. benzene, toluene, ethylbenzene and xylene) was monitored inside and outside the Sanctuary of the Beata Vergine dei Miracoli (Saronno, Italy) by passive air sampling (using radial samplers), during two sampling periods in 2022 (April 27-May 11 and May 11-May 25). Concurrently, particulate matter (PM10 and PM2.5) concentration was determined by using sensors based on laser scattering technology. Moreover, we took advantage of the location of this sanctuary (proximity to an air monitoring central unit) to evaluate the performance of a new axial-type sampler for nitrogen dioxide. Sulphur dioxide concentration was in the range 0.8–3.1 µg/m3, with outdoor values higher than indoor ones, whereas no detectable concentrations of hydrogen sulphide were found (<0.1 µg/m3). A different trend was observed for ammonia, where indoor concentrations were higher than outdoor ones (3.6–5.5 vs 2.7–3.1 µg/m3). Among BTEX, only for benzene statistical differences (p-value < 0.05) between the indoor and outdoor concentrations were found, suggesting additional indoor sources for this hydrocarbon. Toluene results as the most abundant among these hydrocarbons (1.7–2.3 µg/m3) in outdoor environments, whereas, in indoor environments, benzene has the higher concentration (6–9 µg/m3). The indoor concentrations of NO 2 (15.4–22.2 µg/m3 for radial samplers and 48–60 µg/m3 for axial samplers) are not only much higher than the recommended limit values to guarantee proper conservation of artefacts (5 µg/m3), but also close to the values for human health preservation (40 µg/m3), suggesting the need of proper strategy to improve indoor air quality inside the Sanctuary. The preliminary results obtained for the new axial-type sampler for nitrogen dioxide are very encouraging, given the closeness of the obtained concentration data to those measured by monitoring central unit, even though further experiments will have to be carried out to validate this sampler. [ABSTRACT FROM AUTHOR]

The semantic segmentation of outdoor LiDAR point clouds is one of the gigantic fields in the large-scale driving scenario. However, the performances of the state-of-the-art methods are unsatisfactory caused by the intrinsic limitations of the outdoor point clouds with excessive distribution of sparsity and imbalanced distribution of density, both of which become great challenges for a precise segmentation of LiDAR point clouds. To tackle the aforementioned intrinsic problems of point clouds with an improved segmentation performance, we propose a brand new attention-based feature interaction module called Voxel Slicing and Interaction based Attention Module (V-SIAM) that is integrated into the segmentation networks. Our V-SIAM is composed of a Voxel Slicing and Interaction Module (V-SIM) followed by a Voxel Attention Module (VAM), where the V-SIM is utilized to significantly reduce the negative impact caused by the imbalanced point density, in terms of enhancing the interaction of the voxel feature by a novel feature slicing, leading to the enriched voxel feature details of the point clouds. Moreover, the VAM is utilized to reduce the negative effect caused by the excessive sparsity of the point clouds, in terms of recalibration among voxel features via an innovative way, leading to the extraction of adaptive and self-enhanced voxel features. Besides the V-SIAM, a Multi-scale Voxel Feature Extractor (MsVFE), utilized as the preprocessing module of the segmentation networks, is proposed to further alleviate the negative influence caused by the excessive sparsity of the point clouds, realized by fusing the multi-scale voxel information of the sparse point clouds, leading to extraction of more detailed features of the point clouds. Our experimental results show that the proposed methods achieve 73.7% mIoU on the large-scale SemanticKITTI benchmark, outperforming the state-of-the-art PVKD and 2DPass by +1.3% mIoU and +0.8% mIoU, respectively. Moreover, our proposed MsVFE and V-SIAM achieve the state-of-the-art performance on the Toronto3D dataset and KITTI-360 dataset. • V-SIAM enrichs voxel feature details and recalibrates the voxel features. • MsVFE fuses multi-scale voxel information of the sparse points. • Our methods achieve SOTA performances on Toronto3D and KITTI-360 datasets. [ABSTRACT FROM AUTHOR]

• We propose a multi-scale feature fusion method from different resolution feature maps for 3D object detection. • We propose a deep multimodal fusion method to make full use of the advantages of RGB image data and point clouds. • The experimental evaluation on the benchmark KITTI achieves the state-of-the-art performance. The perception of 3D objects in the scene is the basis of autonomous driving. Most autonomous driving cars are equipped with cameras and Lidar to obtain 3D spatial information. RGB images taken from the camera and point cloud produced by Lidar both have their own advantages for 3D object detection. In order to make better use of the advantages of image data and point cloud data, a 3D object detection method based on Deep Multi-scale and Multi-modal Fusion (DMMF) is proposed. Firstly, point cloud is projected to the Bird's Eye View (BEV) and extract BEV map and RGB image feature with feature extractor, respectively. Then, fuse the multi-modal feature with the deep multi-scale fusion method and finally input to position regression and classification network for object classification and accurate positioning. The experimental results on the benchmark KITTI dataset show that the method reaches state-of-the-art in both car and pedestrian classes, especially for hard level data, the detection AP is significantly improved. [ABSTRACT FROM AUTHOR]

• LiDAR-based roughness parameterization is introduced in Evapotranspiration (ET) model. • Original SEBS overestimate LE and underestimate H compared to in-situ EC observations. • SEBS LiDAR perform better than SEBS in estimating LE and H compared to EC observations. • Canopy height controls the output of SEBS ET model. • LiDAR can significantly improve the accuracy of roughness lengths in ET model. Spatially distributed information of actual Evapotranspiration (ET) is essentially required for various applications in agriculture, as well as studies related to water balance. An Energy Balance model that uses optical and thermal remote sensing datasets is appropriate for the spatially distributed estimation of ET, which is primarily controlled by local environmental conditions. In this study, we use the Surface Energy Balance System (SEBS), which is an Energy Balance model, to estimate an improved actual ET, by integrating canopy height information obtained from ICESat/GLAS, the first space-borne LiDAR satellite. Our model estimates latent heat flux (LE) as a residual of energy balance, while sensible heat flux (H) is estimated based on Monin–Obukhov similarity theory. The spatial variability of H and LE estimated from Landsat TM/ETM+ images were compared with eddy covariance (EC) based in situ flux tower observations, in order to document the SEBS model uncertainties in three forest sites in the East and Southeast Asian ecosystem. We identified that canopy height (h c) is one of the critical parameters that can induce uncertainties in SEBS model estimations. Therefore, h c obtained from LiDAR was introduced in parameterization of the roughness length for momentum transfer (z 0m). In situ based h c obtained from flux tower stations, and the normalized difference vegetation index based formulation of h c proposed by Su (2001), were also introduced in the SEBS z 0m parameterization. Root mean square error of LiDAR integrated SEBS (SEBS LiDar) for estimated H values reduced by (20, 45, and 23)% compared to Landsat integrated SEBS (SEBS Landsat), and (18, 7, and 9)% compared to In situ integrated SEBS (SEBS Insitu) at the three selected forest sites (SMC, QYZ, and SMF), respectively, while the mean coefficient of determination values for the estimated LE improved by (9.09 and 5.88)% compared to SEBS Landsat and SEBS Insitu , respectively, with reference to ground-based flux tower observations. Our analysis reveals that LiDAR-based parameterization of z 0m can significantly increase the accuracy of estimated roughness lengths within an ET model, which can therefore improve the accuracy of the estimated turbulent heat fluxes. SEBS LiDAR was further evaluated for the spatially distributed mapping of actual ET. We have observed that ET estimated by SEBS Landsat overestimated in forest sites compared to EC observations; however, LiDAR-integrated SEBS marginally improved the accuracy of ET, thereby leading to an improvement in the mean correlation value of 0.87 and mean normalized standard deviation value of 0.93 approaching unity based on Taylor diagram, which more closely approximates the actual ground-based measurements. The proposed methodology overcomes the current limitation in the energy balance models in estimating roughness lengths, by providing accurate formulation of h c by specifically incorporating LiDAR measurements in areas of tall vegetation canopies. The operational improvement in roughness lengths by next-generation SEBS LiDAR model provides an opportunity to improve the estimation accuracy of water and energy fluxes, specifically ET. Overall, our proposed SEBS LiDAR model provides a benchmark that is useful to policy makers and water resource managers in devising a plan for sustainable use of water resources in the East Asian region, as well as other similar ecosystems. [ABSTRACT FROM AUTHOR]

A recently proposed analytical wake model for a horizontal axis utility scale wind turbine is revisited, and revised and improved. The model is based upon conservation of momentum in the context of actuator disc theory, and the assumption of a distribution of the double-Gaussian type for the velocity deficit in the wake. The model is developed and improved and reveals characteristics of the wind turbine wake velocity deficit for the full wake, including the near-wake to within close proximity of the wind turbine rotor. Full 2-dimensional model fitting to lidar wake measurement data obtained from a 5 MW utility scale wind turbine is carried out for the full range of inflow wind velocities of primary interest. Such a full wind turbine wake model has the potential to facilitate analytic calculations within the wind turbine wake region, and the potential to improve understanding of wind turbine aerodynamics. • Double-Gaussian analytical wake model for a horizontal axis wind turbine. • Wake model validation for utility scale wind turbine. • Characteristics of wind turbine wake velocity deficit modelled. [ABSTRACT FROM AUTHOR]

High resolution, accurate strain sensors find vital applications in civil, aerospace, and mechanical engineering. Photonic solutions, especially fibre Bragg gratings, despite being promising platforms for strain sensing in harsh environments, and achieving microstrain resolution, suffer from strong sensitivity to temperature fluctuations and require expensive optical detection methods. To tackle these challenges, in this work we present a mode-locked fibre laser strain sensor based on intracavity pulse interference. Our all-fibre sensor, using an intracavity Mach-Zehnder interferometer architecture achieves 20 microstrain resolution with linear response over a 4 millistrain range. Our proposed sensor does not require external locking, and it is environmentally stable, decoupling temperature and strain effects. Furthermore, through a full electronic read-out in radio-frequency domain, our solution can bypass expensive and bulky optical detection. These features pave the way for low-cost and robust photonic strain sensors technology with disruptive real world impact. [ABSTRACT FROM AUTHOR]

Measuring a large grid structure is always based on the traditional total station, which requires a significant workforce. Aiming at the ring structure's characteristics, this paper fully uses multi-sensor advantages and proposes an automatic identification method of grid structure nodes. This method combines deep learning, panoramic camera, and laser scanning and improves the existing method of measuring the lifting process of the grid structure. The main contributions of this paper are as follows: (1) Aiming at the issue with existing measurement methods relying on complex multi-camera systems, a method based on a single panoramic camera and deep learning is proposed to quickly complete the full-field image target points detection, which provides a basis for the identification of target areas in the point clouds; (2) For the challenge posed by a large number of point clouds models and complex processing, a method of automatically capturing point clouds nodes by integrating panoramic camera and laser scanning was proposed. The projection points of the interest region of the image corresponding to the target region in the point clouds and precise identification of nodes were completed; (3) A node classification method and optimization method based on density peak clustering was proposed to solve the problem of node aliasing and processing node center in some areas of interest, for the classification and displacement calculation of grid structure nodes. The proposed method is validated on the scale frame model of a gymnasium, demonstrating the practicability of the proposed approach. • Aiming at the issue with existing measurement methods relying on complex multi-camera systems, a method based on a single panoramic camera and deep learning is proposed to complete image target detection, which provides a basis for the identification of target areas in the points cloud; • Aiming at the challenge posed by a large number of point clouds models and complex processing, a method of automatically capturing point clouds nodes by integrating panoramic camera and lidar was proposed. • A node classification method and optimization method based on density peak clustering was proposed to solve the problem of node aliasing and processing node center in some areas of interest, for the classification and displacement calculation of cable network structure nodes. [ABSTRACT FROM AUTHOR]

A near-infrared (NIR) distributed feedback (DFB) laser sensor based on long optical path absorption spectroscopy technique was developed for simultaneous measurement of atmospheric methane (CH 4), carbon dioxide (CO 2) and oxygen (O 2). A fiber-optic switch (FOS) in conjunction with the time-division multiplexing (TDM) technique was adopted to enable different lasers scan over the target gas absorption lines in turn. The long optical path wavelength modulation spectroscopy (WMS) technique with second harmonic detection as well as a laser power fluctuation correction method was used to improve the sensor's precision and accuracy. For the selected gas absorption lines, located at 6046.945 cm-1, 6361.245 cm-1 and 7877.647 cm-1, the detection limit (1σ) of 0.034 parts per million (ppm) for CH 4 , 11.921 ppm for CO 2 and 0.14% for O 2 was achieved according to the concentration and noise level. Allan deviation analysis indicates that the 1-s measurement precision was 0.030 ppm for CH 4 , 11.518 ppm for CO 2 and 0.14% for O 2 , which could be improved to 6.5E-4 ppm, 0.255 ppm and 0.005% at an optimal average time of 800 s. A two-day continuous measurement of the CH 4 , CO 2 , and O 2 concentration from ambient air was carried out, which verifies the stability and robustness of the developed multi-gas sensor. • A novel optics multiplexing structure was designed for multi-gas sensing. • A homologous ratio method was proposed to correct the laser power fluctuation. • Simultaneous atmospheric CH 4 , CO 2 and O 2 detection in NIR was realized. [ABSTRACT FROM AUTHOR]

We design a blade pitch controller employing linear parameter-varying (LPV) synthesis techniques for a floating hydrostatic wind turbine (HWT) with a barge platform, which is based on the LIDAR (Light Detection and Ranging) preview on the wind speed. The developed control system can simultaneously reduce barge pitch motions and regulate the power in Region 3. These two functions would normally disturb each other if designed separately. The state space model is not affinely dependent on the wind speed thus the LPV controller is obtained by satisfying multiple LMIs evaluated at a set of gridded points within the wind speed range in Region 3. An anti-windup compensation scheme is then used to improve the LPV controller's performance when the pitch undergoes saturation around the rated wind speed. The simulations based on a high-fidelity barge HWT model show that our pitch controller significantly reduces barge pitch motions, loads on blade bearings & tower, and generator power fluctuations, compared with a gain-scheduled PI pitch controller. • An LPV blade pitch controller based on LIDAR preview is developed. • The developed control system can simultaneously reduce the barge pitch motions and regulate the power in Region 3. • Effectiveness of the designed control system is assessed on a high-fidelity barge hydrostatic wind turbine simulation model. [ABSTRACT FROM AUTHOR]

Recently, two types of common sensors, LiDAR and Camera, show significant performance on all tasks in 3D vision. LiDAR provides accurate 3D geometry structure, while camera captures more scene context and semantic information. The fusion of two different sensor becomes a fundamental and common idea to achieve better performance. To give a thorough cognition of the complementary and boosting about two kind of sensors. This paper briefly reviews the fusion and enhancement systems between both two sensors in the field of depth completion, 3D object detection, 2D\3D semantic segmentation and 3D object tracking. Meanwhile, the state of art fusion algorithms is quantitatively demonstrated, in this paper, based on the in KITTI widely-used public dataset. Furthermore, the technical challenge and the future potential of LiDAR and camera fusion are also discussed. [ABSTRACT FROM AUTHOR]

A new method for measurement of the pitch deviation of a scale grating by an optical angle sensor based on the laser autocollimation is proposed. In the proposed method, the scale pitch deviation is evaluated through detecting the change in the angles of diffraction of the first-order diffracted beams, which is associated with the change in the scale pitch deviation. The optical angle sensor unit is designed to measure the angles of diffraction of both the positive and negative first-order diffracted beams simultaneously so that the influences of the scale form error and the roll error motion of the linear slide for the scanning of a linear scale can be canceled through the differential operation. The optical angle sensor unit based on the laser autocollimation is newly designed and developed, and the pitch deviation of a linear scale from a commercial interferential scanning-type linear encoder is evaluated by the developed setup to verify the feasibility of the proposed method in experiments. The scale form error obtained through the differential operation is also compared with the one evaluated by a commercial Fizeau interferometer. Furthermore, the uncertainty analysis on the proposed pitch deviation measurement is carried out based on GUM to theoretically verify the feasibility of the proposed method. • A new method for measurement of grating pitch deviation is proposed. • The method uses optical angle sensors based on the laser autocollimation. • Both the positive- and negative diffracted beams are employed. • Scale angular error motions does not affect measurement. • Feasibility of the proposed method has been verified in experiments. [ABSTRACT FROM AUTHOR]

This paper presents a real-time 3D object detector based on LiDAR based Simultaneous Localization and Mapping (LiDAR-SLAM). The 3D point clouds acquired by mobile LiDAR systems, within the environment of buildings, are usually highly sparse, irregularly distributed, and often contain occlusion and structural ambiguity. Existing 3D object detection methods based on Convolutional Neural Networks (CNNs) rely heavily on both the stability of the 3D features and a large amount of labelling. A key challenge is efficient detection of 3D objects in point clouds of large-scale building environments without pre-training the 3D CNN model. To project image-based object detection results and LiDAR-SLAM results onto a 3D probability map, we combine visual and range information into a frustum-based probabilistic framework. As such, we solve the sparse and noise problem in LiDAR-SLAM data, in which any point cloud descriptor can hardly be applied. The 3D object detection results, obtained using both backpack LiDAR dataset and the well-known KITTI Vision Benchmark Suite, show that our method outperforms the state-of-the-art methods for object localization and bounding box estimation. [ABSTRACT FROM AUTHOR]

Accurate registration of light detection and ranging (LiDAR) point clouds and images is a prerequisite for integrating the spectral and geometrical information collected by low-cost unmanned aerial vehicle (UAV) systems. Most registration approaches take the directly georeferenced LiDAR point cloud as a rigid body, based on the assumption that the high-precision positioning and orientation system (POS) in the LiDAR system provides sufficient precision, and that the POS errors are negligible. However, due to the large errors of the low-precision POSs commonly used in the low-cost UAV LiDAR systems (ULSs), dramatic deformation may exist in the directly georeferenced ULS point cloud, resulting in non-rigid transformation between the images and the deformed ULS point cloud. As a result, registration may fail when using a rigid transformation between the images and the directly georeferenced LiDAR point clouds. To address this problem, we proposed NRLI-UAV, which is a non-rigid registration method for registration of sequential raw laser scans and images collected by low-cost UAV systems. NRLI-UAV is a two-step registration method that exploits trajectory correction and discrepancy minimization between the depths derived from structure from motion (SfM) and the raw laser scans to achieve LiDAR point cloud quality improvement. Firstly, the coarse registration procedure utilizes global navigation satellite system (GNSS) and inertial measurement unit (IMU)-aided SfM to obtain accurate image orientation and corrects the errors of the low-precision POS. Secondly, the fine registration procedure transforms the original 2D-3D registration to 3D-3D registration. This is performed by setting the oriented images as the reference, and iteratively minimizing the discrepancy between the depth maps derived from SfM and the raw laser scans, resulting in accurate registration between the images and the LiDAR point clouds. In addition, an improved LiDAR point cloud is generated in the mapping frame. Experiments were conducted with data collected by a low-cost UAV system in three challenging scenes to evaluate NRLI-UAV. The final registration errors of the images and the LiDAR point cloud are less than one pixel in image space and less than 0.13 m in object space. The LiDAR point cloud quality was also evaluated by plane fitting, and the results show that the LiDAR point cloud quality is improved by 8.8 times from 0.45 m (root-mean-square error [RMSE] of plane fitting) to 0.05 m (RMSE of plane fitting) using NRLI-UAV, demonstrating a high level of automation, robustness, and accuracy. [ABSTRACT FROM AUTHOR]

Torque is one of the mighty core parameters of rotating machinery, and it has profound implications for the success of many engineering and industrial manufacturing activities. To date, more and more torque sensors are being researched and manufactured to meet the requirements of torque measurements in all of the engineering sectors that rely on accurate measurement of torque parameters for their success. A direct implication of these constantly growing developments is the recent development of various torque sensors, which operate based on more than ten different principles. Although several measurement principles have been created, torque sensors themselves can only be divided into two categories based on whether they come into contact with the rotating objects being measured (contact sensors) or do not have any point of contact with these objects (contactless sensors). In this paper, the concept of contactless sensors for torque measurement is redefined and the major classes of torque sensors (i.e., contactless sensors based on the laser Doppler effect, laser speckle effect, and optical coherence vibrometer, etc.) are introduced outlining the main progress, challenges, and future directions involving torque measurement applications. As the key to the success of torque measurement systems, some advanced signal processing techniques that are commonly used to enhance the accuracy of the torque measurement data are also introduced. [ABSTRACT FROM AUTHOR]

In this paper, we present a more efficient strategy than existing solutions to enhance the lateral resolution of low photon 3D-LiDAR operating in Geiger mode. Our pipeline makes it possible to reconstruct 3D-images with an unprecedented lateral-resolution, simultaneously at low photon count and Hertz level framerates. It is applied on simulated GmAPD 3D-LiDAR signals. Signals acquired using these kind of sensors are unsuitable for direct applications of Compressive Sensing algorithms. Our contribution focuses on a more efficient strategy for waveform denoising and reconstruction. For each pixel, we reconstruct sub-pixels with a Compressive Sensing approach. After describing our method, we demonstrate its applicability on realistic simulated data. • Proposed methodology increases the lateral resolution of SWIR-optimized GmAPD. • New statistical method defined to denoise data from active imaging systems. • Method is suitable for embedded systems and on board post-processing. [ABSTRACT FROM AUTHOR]

Understanding the impacts of 2D/3D urban structure on PM 2.5 is critical for protecting resident health and sustainable development. However, accurate understanding is limited by the lack of high-resolution PM 2.5 simulation in cities with sparse monitoring stations and the simultaneously consideration of different landscape types both in 2D and 3D directions. Using Yinchuan City as a case study, an integrated simulation model combining satellite AOD, NDVI and meteorological data was developed using random forest (RF) algorithm to simulate city-scale PM 2.5 variation at 30 m resolution. Then, the most critical 2D/3D urban metrics in altering PM 2.5 and their impacts were explored through RF analysis. Finally, optimal urban structure was identified using Bayesian Network and multi-scenario analysis. The results indicated that 1) the established RF model could effectively simulate within-city variation of PM 2.5 at 30 m with an R2 of 0.75. Thus, it is feasible to use the abundant temporal information and high-resolution AOD to map city-scale PM 2.5 and overcome the limitation of spare monitoring stations. 2) The emission-related metric of distance to polluting enterprise and city center had the largest impacts on PM 2.5 , followed by NDVI, building height, impervious surface area and shape, and UGS green volume, area, shape and connectivity. Optimized 3D urban structure for reducing PM 2.5 was proposed from the three aspects of emission source location, UGS pattern and impervious surface. This study deepens the understanding of how 2D/3D urban structure impact PM 2.5 and provides scientific references for optimization possibility of urban air quality in both horizontal and vertical directions. [ABSTRACT FROM AUTHOR]

The detection principle of a multi-longitudinal-mode (MLM) Doppler lidar based on a quadri-channel Mach–Zehnder interferometer (QMZI) was analysed. The measurement error formulas for the radial wind speed and backscatter ratio of this type of Doppler lidar system were derived. The advantages of this detection technology were thoroughly analysed based on the principle. The optical path difference (OPD) ' l ' between the two interference arms of the QMZI was optimised. For a detection height of 0–20 km, ' l ' was selected as 50 cm to balance the detection of radial wind speed and backscatter ratio. The overall structure of the MLM Doppler lidar based on QMZI was designed, and detailed design parameters of the system were provided. Further simulation was conducted to evaluate the detection performance of the designed QMZI- based MLM Doppler lidar system. The simulation results showed that when using a pulse laser with a power of 17.5 W@355 nm and a telescope with an aperture of 25 cm, a detection zenith angle of 30°, a vertical resolution of 26 m@0–10 km and 52 m@10–20 km and a pulse accumulation time of 1 min, the radial wind speed measurement errors of the system during the day and night were less than 2.1 m/s and 2.5 m/s; the backscatter ratio relative measurement errors were less than 0.81% and 0.97%. • The detection principle of QMZI based MLM Doppler lidar is analysed. • The lidar measurement error formulas for radial wind speed and backscatter ratio are derived. • The optimised OPD of QMZI two arms is 50 cm for both wind speed and backscatter ratio measurements at height of 0–20 km. • The detailed design parameters of the lidar system are provided. • The performance simulation and error analysis of the designed lidar system are performed. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) technology has been extensively used to collect precise geometric information. However, conventional LiDAR sensors are restricted to single-wavelength operation, limiting their ability to capture valuable spectral information. Multispectral LiDAR (MSL) systems, on the other hand, have emerged as a promising solution by facilitating the active acquisition of spatial and spectral data. These advanced systems exhibit tremendous potential in facilitating advanced analysis and classification of seafloor sediments. In this study, the first demonstration of an MSL system optimized for investigating the feasibility of underwater ore classification using a tunable laser source is described. The MSL prototype features a spectral resolution of 10 nm, and 11 spectral channels, covering the range from 460 to 560 nm. Laboratory-based experiments were conducted to evaluate the accuracy of range measurements and the classification performance of the system. The spectral profiles of eight distinct ore samples acquired by the MSL were utilized for classification using four classification methods: naive Bayes (NB), k Nearest Neighbor (KNN), support vector machine (SVM), and random forest (RF). Furthermore, comparative analyses were conducted to investigate the classification enhancements realized by leveraging the MSL system with multiple spectral channels as opposed to single-wavelength and dual-wavelength systems. • Tunable laser-based Multispectral Lidar enables active acquisition of spatial and spectral data with a single detector. • Wide spectral coverage from 460 nm to 560 nm with 10 nm increments. • Feasibility study of Multispectral Lidar for underwater ore classification. • Enhanced classification performance through the integration of additional wavelength bands. [ABSTRACT FROM AUTHOR]

In this study, a graphene electrochemical sensor based on laser graphene polymer material was proposed to induce graphene formation on polyimide substrates via fiber laser. The laser produces stable power and results to achieve the benefits of consistency, conductivity, and flexibility. The electrochemical three-electrodes were manufactured on polyimide to replace the traditional three-electrodes by achieving small size and portability. An electrode activation is the modification of laser-scribed graphene electrodes (LSG) to facilitate the binding of liver cancer sites. The evaluation is performed by differential pulse Voltammetry (DPV) to detect cancer proteins in the phosphate buffer saline (PBS) buffer and serum. In a serum environment, the concentrations of alpha-fetoprotein (AFP) and Carcinoembryonic antigen (CEA) were detected from 0.75 ng ml−1 to 100 ng ml−1, AFP and CEA electrodes have a good linear range (R2 = 0.96 and R2 = 0.98), indicating the sensor's sensitivity and specificity for cancer detection. In addition, two types of carcinogenic proteins were monitored in the PBS and successfully detected in this experiment. Based on the results, the appropriate LSG sensor may be used for monitoring with limited resources. Electrode manufacturing is simple, fast, low-cost, small in size, convenient to carry, stable, instant detection, and flexible. Electrochemical aptasensing of CEA and AFP through aptamer-modified LSG biosensing devices in various real-time samples. [Display omitted] • Laser-scribed graphene electrodes (LSG) were fabricated through fiber laser. • Aptamer-modified LSG electrode was employed for the detection of cancer biomarkers. • Successfully implemented in the electrochemical detection of CEA and AFP. • An excellent detection limit of 13.68 pg mL−1 and 50.66 pg mL−1 for AFP and CEA. • Point of care devices candidate for the evaluation of cancer biomarkers. [ABSTRACT FROM AUTHOR]

We present and demonstrate a cascaded laser scan architecture to achieve a three-fold gain in scan angle. We cascaded multiple piezoelectric MEMS scanners using a high numerical aperture relay lens pair in between scanners, which are located at optical conjugate planes. The cascaded scan architecture comprising 3 scanners is simulated using ray-tracing software. Finally, we conduct experiments using the folded architecture, where the incoming laser beam bounces off the first scan mirror (twice) and the second scan mirror, having nearly identical resonant frequencies of 27.59 kHz, yielding a total optical scan angle of 90°. The observed scan angle exceeds the angle achieved by any single MEMS scanner. One can exploit the use of the back surface of the mirrors, as well as higher numerical aperture lenses to improve the achievable total optical scan to more than double, up to 210°. With further development, the proposed architecture can be integrated into LiDAR systems to achieve high number of resolvable spots on the target, with unprecedented speed and form factor, far beyond what can be achieved with rotating motor or polygon mirror-based architectures. • We present a cascaded laser scan architecture for a three-fold gain in scan angle towards LIDAR applications. • Three piezoelectric MEMS scanners were cascaded with relay lens pairs. • A total optical scan angle of 90 degrees was achieved. • One can achieve 180 degrees of scan angle through employing the back surfaces of the scanners. • The demonstrated scan exhibits an unprecedented number of resolvable spots on the target. [ABSTRACT FROM AUTHOR]

Quantum sensing, built upon fundamental quantum phenomena like entanglement and squeezing, is revolutionizing precision and sensitivity across diverse domains, including quantum metrology and imaging. Its impact is now stretching into radar and LiDAR applications, giving rise to the concept of quantum radar. Unlike traditional radar systems relying on classical electromagnetic, quantum radar harnesses the potential of the quantum properties of photon states like entanglement and quantum superposition to transcend established boundaries in sensitivity and accuracy. This comprehensive review embarks on an exploration of quantum radar and quantum LiDAR, guided by two primary objectives: enhancing sensitivity through quantum resources and refining accuracy in target detection and range estimation through quantum techniques. We initiate our exploration with a thorough analysis of the fundamental principles of quantum radar, which includes an evaluation of quantum illumination protocols, receiver designs, and their associated methodologies. This investigation spans across both microwave and optical domains, providing us with insights into various experimental demonstrations and the existing technological limitations. Additionally, we review the applications of quantum radar protocols for enhanced accuracy in target range determination and estimation. This section of our review involves a comprehensive analysis of quantum illumination, quantum interferometry radar, and other quantum radar protocols, providing insights into their contributions to the field. This review offers valuable insights into the current state of quantum radar, providing a deep understanding of key concepts, experiments, and the evolving landscape of this dynamic and promising field. [ABSTRACT FROM AUTHOR]

This work presents a new depth- and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation in a multi-modal setup between LiDAR and camera sensors. The proposed model leverages scene semantics as a mid-level representation and is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments. We claim that this is the first framework of its kind and it has practical applications in autonomous vehicles such as providing a fail-safe mechanism and augmenting available data in the target image domain. The proposed model is evaluated on the large-scale and challenging Semantic-KITTI dataset, and experimental findings show that it considerably outperforms the original TITAN-Net and other strong baselines by 23.7% margin in terms of IoU. • Domain translation between LiDAR and RGB space. • Improvement of over 23% compared with previous state of the art. • Sensor Failure in autonomous vehicles. • Depth estimation. [ABSTRACT FROM AUTHOR]

Modern agriculture is heavily reliant on precision agriculture. To ensure the uniformity of seeding, inexpensive, and easy-to-use seed counting sensors are required. The information from those sensors must be easily available, transferable, and interpretable while the sensor should be simple, robust, and cheap. This paper described the development and testing of a new line laser based seed counting sensor for use with seed metering devices. The proposed sensor could scan the area (instead of a line) at the release point of the seed metering device. It formed a scanning area using a line laser and 7 phototransistors. The sensor had a HIGH voltage signal level raising time of about 500 ns and did not require additional signal or image processing software. The signal from the phototransistor was combined through the NAND gate and transferred to an Android device. All components used in the sensor were cheap and reliable. The sensor can be used in precision seeders for reliable real-time seed monitoring and fault detection. [Display omitted] • Demand for low-cost seed counting sensor for lab and IoT applications. • Designed and built seed monitoring sensor with laser and 7 phototransistors. • Provided theory and calculation for sensor design. • Tested physical prototype in lab with seed metering device. • Results show accurate readings for real-time seed counting. [ABSTRACT FROM AUTHOR]

Sensitivity is a critical parameter in flexible pressure sensors, which typically refers to the ratio of the change in the output signal to the change in the input pressure signal. In the design of flexible pressure sensors, although the sensitivity can be enhanced by designing the surface structure of the dielectric, such as a single structure like a hemispherical shape, the sensitivity will decrease as the pressure gradually increases. Furthermore, in most reports, the fabrication of electrodes is particularly cumbersome, which increases the difficulty of preparing the sensor. Here, we propose a fabrication method for a flexible capacitive sensor based on direct laser writing (DLW) technology. Controlled UV direct laser writing of polyimide (PI) was used to prepare a series of different two-level protruding structures under different laser parameters. In this type of iontronic sensor, the maximum sensitivity is up to 986.8 kPa−1, and the pressure range exceeds 200 kPa. The response/recovery time can reach 10/16 ms and withstand 2500 cycles of repeated loading/unloading under high pressure of 120 kPa. The sensor has good mechanical properties, and high sensitivity, and has been verified by a few applications on the human body. This work demonstrates a simple and convenient method for fabricating electrodes and microstructures, and provides a new approach for future iontronic sensors fabrication. [Display omitted] • Preparation of wavy electrodes and ionic gel with two-level raised structures by directing laser writing. • The effect of the microstructures prepared by different laser parameters on the sensor is investigated. • The maximum sensitivity is up to 986.8 kPa−1, and the pressure range exceeds 200 kPa. [ABSTRACT FROM AUTHOR]

Based on planar waveguides, refractive index sensors have certain advantages over conventional optical fiber sensors, including better mechanical stability and lower cross-responses to factors such as vibrations. In this study, we report for the first time a microcavity Mach-Zehnder interferometric refractive index sensor fabricated within a planar waveguide. The sensor consists of double-line waveguides made of lithium tantalate crystals written by femtosecond laser and microcavities formed by diamond blade dicing. The refractive index sensitivity of the sensor reaches approximately 1856.1 nm/RIU, with extremely low temperature cross-responses. This work provides a new alternative for developing biosensors based on femtosecond laser-written waveguides. In the future, by selecting appropriate materials and optimizing the structure, the sensing performance of this sensor is expected to be further enhanced. • Asensor was prepared in LiTaO 3 by a combination of femtosecond laser etching and precision diamond blade dicing. The refractive index sensitivity of the sensor reaches approximately 1856.1 nm/RIU. • This is the first reported waveguide sensor based on femtosecond laser writing in crystal waveguides. [ABSTRACT FROM AUTHOR]

Functional laser scribing carbon paper (LSCP) decorated with highly uniform Ni nanoparticles were constructed through a facile electroless plating. The nanocomposites were characterized by high resolution scanning electron microscope, X-ray photoelectron spectroscopy, electrochemical impedance spectroscopy, cyclic voltammetry and chronoamperometry. The results showed high electron transferring kinetics of this sensor, which can be ascribed to their excellent properties such as rich pore channels, excellent structural durability, and large surface area. These properties facilitated mass transfer and electron conductions. Notably, a systematical response surface methodology simulating-modeling-predicting-optimizing design was employed to simulate, model and optimize processing parameters to gain the optimal conductivity of 8.52 × 106 S m−1. The obtained sensor owned high electrochemical activity and wide linear responses (0.80 μM–2.50 mM and 4.50 mM–15.20 mM), low detection limit of 20 nM (S/N = 3) to the glucose detection. The glucose determination in human serum and perspiration samples are also successful. Therefore, LSCP/NN provides an excellent sensing platform towards flexible biosensors in monitoring physical conditions. Disposable, Non-enzymatic and Ultrasensitive Carbon Fiber Paper-based Glucose Sensor has been Constructed by A Theoretical Box-Behnken Modeling-Predicting-Optimizing Design. Image 1 • A disposable, ultrasensitive and highly flexible LSCP/NN glucose biosensor is firstly constructed. • Novel nanoporous Ni networks are integrated with pulsed laser-scribed carbon paper via a rational Box-Behnken modeling-predicting-optimizing design. • The enzyme-free and binder-free LSCP/NN biosensor exhibits a wide linear glucose determination range (0.80 μM–2.50 mM; 4.5 mM–15.2 mM). • Excellent sensitivity (3415 μA mM−1 cm−2), a fast response (<1 s) and ultra-low limit of detection (20 nM). • Favorable flexibility, good accuracy and satisfactory specificity for glucose quantification in human serum and perspiration are indicated by the LSCP/NN biosensor. [ABSTRACT FROM AUTHOR]

Current Real-Time Locating Systems (RTLS) typically deployed in indoor built environments are generally based on wireless technologies, fixed cameras, or Lidar-based Simultaneous Localization and Mapping (SLAM), which generally suffer from the drawbacks of low accuracy, reliance on existing infrastructures that may be not available in the deployed environments, labor-intensive environment instrumentation, or economic infeasibility for wide deployment. By improving an ORB RGB-D SLAM with Occupancy Grid Mapping, this paper proposes a new indoor RTLS that can be readily adapted and deployed for a broad range of indoor locating applications while overcoming the limitations faced by current solutions. In addition to the sparse feature map that is maintained by ORB SLAM itself, a new 2D mapping module is developed to build and maintain an additional 2D Occupancy Grid Map (OGM). The 2D OGM is built with the 3D camera poses estimated by Visual SLAM (vSLAM) and laser scans extracted from the point cloud observed by the camera from those poses. In addition, the Robot Operating System (ROS) visualization tools are used to overlay real-time current camera poses and observations (virtual laser scans) on the OGM. This approach not only provides more intuitive pose information to users and allows them to interact with the system, but also enables path planning and continuous navigation, which cannot be implemented directly on vSLAM's original feature map. The localization accuracy of the proposed system is experimentally evaluated with a set of visual landmarks that are installed in a large-scale building environment. The achieved marker position measurement accuracy ranges from 0.039 m to 0.186 m and the marker distance measurement accuracy ranges from 0.018 m to 0.235 m, proving the method's feasibility and applicability in providing real-time and accurate localization for a wide range of applications within constructed facilities and the built environment. Three examples are provided to highlight such potential applications, including path planning and real-time navigation, geo-tagged date collection and location-aware point cloud updating. • A high localization accuracy RTLS is proposed, overcoming other methods' limitations. • The RTLS can work in SLAM or localization mode and switch between them as needed. • The RTLS uses the respective advantages of a sparse map and an Occupancy Grid Map. • A landmark-based algorithm was designed to evaluate the RTLS. • Three examples were described to show the potential applications of the RTLS. [ABSTRACT FROM AUTHOR]

Abstract Compared with dual-channel Fabry-Perot interferometer (DFPI), dual-channel Mach-Zehnder interferometer (DMZI) is a new frequency discriminator for Doppler wind lidar based on double-edge detection. The double-edge frequency discrimination systems based on DFPI and DMZI in the wind lidar system are designed, respectively, and the detection principle is analyzed theoretically. Under certain simulation conditions, performances such as line-of-sight (LOS) wind velocity measurement sensitivities, signal-to-noise ratios (SNR) and LOS wind velocity measurement errors of both DFPI and DMZI systems are simulated and compared based on the U.S. standard atmospheric model. The simulation results show that for 1064 nm aerosol system, the LOS wind velocity measurement sensitivity of the DMZI system is lower than that of DFPI system and the error is higher than that of DFPI system with the same dynamic wind velocity measurement range. Nevertheless, for 355 nm atmospheric molecular system, DMZI system provides a factor of 1.33 times lower error in the LOS wind velocity than that of DFPI with an altitude of 30 km and the LOS wind velocity of 200 m/s. Furthermore, in the LOS wind velocity range of −200 m/s∼ +200 m/s, the measurement error of DMZI system hardly increases with the increasing of wind speed. Therefore, the results of this study not only provide guidance for the selection of discriminators, but also provide theoretical support for the application of DMZI in the double-edge detection of atmospheric molecular lidar. [ABSTRACT FROM AUTHOR]

• A calibration for structured light sensors based on laser line projection is proposed. • The method is based on a plate with protruding cylinders • The calibration error obtained is 0.027 mm. • Multiple cameras can be calibrated simultaneously in only 30 ms. • Heavy noise in the profiles does not affect the calibration results. Accurate camera calibration is a challenging task required for 3D reconstruction sensors. In structured light sensors based on laser line projection, it is necessary to determine the position and orientation of the camera relative to the laser plane. The standard approach based on laser plane fitting requires a difficult setup, particularly in multi-camera configurations. This work proposes a calibration method based on a calibration plate with protruding cylinders. The projection of the laser line on the calibration plate produces a set of partial contours of ellipses, resulting from the projection of the cross section of the cylinders. These contours are used to calculate the position and orientation of the camera accurately. The proposed procedure is divided in two steps: a coarse calibration based on a robust estimator, which provides a rough approximation, and a fine calibration that iteratively optimizes the solution. The result is an accurate and robust procedure that can be applied to multiple cameras simultaneously on devices with limited computational power. Extended tests are used to validate the procedure with test pieces. Results indicate the calibration can be performed in 30 ms, producing a calibration error of only 0.027 mm. [ABSTRACT FROM AUTHOR]

Abstract Point cloud registration is a fundamental problem in 3D scanning. In this paper, we address the frequent special case of registering terrestrial LiDAR scans (or, more generally, levelled point clouds). Many current solutions still rely on the Iterative Closest Point (ICP) method or other heuristic procedures, which require good initializations to succeed and/or provide no guarantees of success. On the other hand, exact or optimal registration algorithms can compute the best possible solution without requiring initializations; however, they are currently too slow to be practical in realistic applications. Existing optimal approaches ignore the fact that in routine use the relative rotations between scans are constrained to the azimuth, via the built-in level compensation in LiDAR scanners. We propose a novel, optimal and computationally efficient registration method for this 4DOF scenario. Our approach operates on candidate 3D keypoint correspondences, and contains two main steps: (1) a deterministic selection scheme that significantly reduces the candidate correspondence set in a way that is guaranteed to preserve the optimal solution; and (2) a fast branch-and-bound (BnB) algorithm with a novel polynomial-time subroutine for 1D rotation search, that quickly finds the optimal alignment for the reduced set. We demonstrate the practicality of our method on realistic point clouds from multiple LiDAR surveys. [ABSTRACT FROM AUTHOR]

Abstract The potential of X-band interferometric synthetic aperture radar (InSAR) heights (e.g. from the TanDEM-X mission) for vegetation canopy height estimation has long been recognized. However, the penetration of the X-band into the canopy results in a height bias and substantially affects this estimation. The aim of the study was to apply a physical model to compensate the penetration depth (i.e. height bias) in canopy height estimation and evaluate its performance. We applied a penetration depth model on different TanDEM-X data in three German forests. This model is based on the volume coherence and imaging geometry of the InSAR acquisitions. We extracted the volume coherence from the TanDEM-X data and retrieved the height bias based on the penetration depth compared to actual surface heights. The modeled height bias was used to compensate the height bias in InSAR heights. The corrected TanDEM-X heights were evaluated with LiDAR data. In general, the penetration depth compensation in the InSAR heights improved the performance compared to the original InSAR heights resulting in elevations with a lower root mean squared error and the mean error decreased to less than 1 m with the LiDAR heights used as reference. This suggested that the height bias was accurately modeled for temperate forests, which can be of high relevance when InSAR heights are used for canopy height estimation or used in multi-temporal analysis such as forest growth, degradation and deforestation monitoring. [ABSTRACT FROM AUTHOR]