Artificial Targets Research Articles

Capabilities of an Acoustic Camera to Inform Fish Collision Risk with Current Energy Converter Turbines

A diversified energy portfolio may include marine energy in the form of current energy converters (CECs) such as tidal or in-river turbines. New technology development in the research stage typically requires monitoring for environmental effects. A significant environmental effect of concern for CECs is the risk of moving parts (e.g., turbine blades) colliding with animals such as fishes. CECs are installed in energetic locations in which it is difficult to operate sensors to fulfill monitoring requirements for informing collision risk. Collecting data (i.e., about blade strikes or near-misses) that inform interactions of fishes with CECs is usually attempted using active acoustic sensors or video cameras (VCs). Limitations of low-light conditions or water turbidity that preclude effective use of VCs are overcome by using high-resolution multibeam echosounders (or acoustic cameras (ACs)). We used an AC at two sites to test its ability to detect artificial and real fish targets and determine if strike, near-miss, and near-field behavior could be observed. Interactions with fish and artificial targets with turbines have been documented but strike confirmation with an AC is novel. The first site was in a tidal estuary with a 25 kW turbine and water clarity sufficient to allow VC data to be collected concurrently with AC data showing turbine blade strike on tethered artificial fish targets. The second site was a turbid, debris-laden river with a 5 kW turbine where only AC data were collected due to high water turbidity. Data collection at the second site coincided with downstream Pacific salmon (Oncorhynchus spp.) smolt migration. Physical fish capture downstream of the turbine was performed with an incline plane trap (IPT) to provide context for the AC observations, by comparing fish catches. Discrimination between debris and fishes in the AC data was not possible, because active movement of fishes was not discernable. Nineteen fishes were released upstream of the turbine to provide known times of possible fish/turbine interactions, but detection was difficult to confirm in the AC data. ACs have been used extensively in past studies to count large migratory fish such as Pacific salmon, but their application for small fish targets has been limited. The results from these two field campaigns demonstrate the ability of ACs to detect targets in turbid water and observe blade strikes, as well as their limitations such as the difficulty of distinguishing small fishes from debris in a high-energy turbid river. Recommendations are presented for future applications associated with CEC device testing.

A computer vision-based method for bridge model updating using displacement influence lines

This paper presents a new computer vision-based method that simultaneously provides the moving vehicle’s tire loads, the location of the loads on a bridge, and the bridge’s response displacements, based on which the bridge’s influence lines can be constructed. The method employs computer vision techniques to measure the displacement influence lines of the bridge at different target positions, which is then later used to perform model updating of the finite element models of the monitored structural system.The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the co-authors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads.The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements.The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system’s modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge’s displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model.The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring.

Amplifying the Sensitivity of Polydiacetylene Sensors: The Dummy Molecule Approach.

There is an increasing need for fast and accurate assessment of various health conditions, where polydiacetylenes (PDA), having unique stress-sensitive optical properties, have great potential. When the conjugated backbone of PDA is disturbed by steric repulsion between the receptor-target complexes formed at the PDA surface via specific recognition events, the bandgap of PDA increases and produces color change and fluorescent emission as a dual sensory signal. However, this detection mechanism suggests an intrinsic sensitivity limit of PDA platform because unless adjacent receptors are occupied by target molecules no signal is anticipated. A novel approach to improve the sensitivity and limit of detection of PDA sensors has been developed by preoccupying the surface of PDA liposomes with an optimized amount of artificial target molecules named as dummy molecules. The sensitivity and limit of detection (LOD) showed large improvement by the surface-bound dummy molecules. In addition, the dummy strategy was synergically integrated with another sensitivity enhancing method with a different working mechanism in a PDA sensor for Neomycin detection. When optimized, the LOD of the PDA sensor was improved to 7 nM from 80 nM of the control and the signal intensity increased consistently throughout the entire tested concentration range of the target Neomycin. Finally, the general applicability of the dummy strategy to other target molecules was successfully confirmed by implementing the dummy strategy in a PDA sensor for Surfactin detection.

On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor.

This study described the on-orbit vicarious radiometric calibration of Chinese civilian high-resolution stereo mapping satellite ZY3-02 multispectral imager (MUX). The calibration was based on gray-scale permanent artificial targets, and multiple radiometric calibration tarpaulins (tarps) using a reflectance-based approach between July and September 2016 at Baotou calibration site in China was described. The calibration results reveal a good linear relationship between DN and TOA radiances of ZY3-02 MUX. The uncertainty of this radiometric calibration was 4.33%, indicating that radiometric coefficients of ZY3-02 MUX are reliable. A detailed discussion on the validation analysis of the comparison results between the different radiometric calibration coefficients is presented in this paper. To further validate the reliability of the three coefficients, the calibrated ZY3-02 MUX was compared with Landsat-8 Operational Land Imager (OLI). The results also indicate that radiometric characteristics of ZY3-02 MUX imagery are reliable and highly accurate for quantitative applications.

Reactivity of NK Cells Against Ovarian Cancer Cells Is Maintained in the Presence of Calcium Phosphate Nanoparticles.

Calcium phosphate nanoparticles (CaP-NPs) are biodegradable carriers that can be functionalized with biologically active molecules. As such, they are potential candidates for delivery of therapeutic molecules in cancer therapies. In this context, it is important to explore whether CaP-NPs impair the natural or therapy-induced immune cell activity against cancer cells. Therefore, in this study, we have investigated the effects of different CaP-NPs on the anti-tumor activity of natural killer (NK) cells using different ovarian cancer (OC) cell line models. We explored these interactions in coculture systems consisting of NK cells, OC cells, CaP-NPs, and therapeutic Cetuximab antibodies (anti-EGFR, ADCC-inducing antibody). Our experiments revealed that aggregated CaP-NPs can serve as artificial targets, which activate NK cell degranulation and impair ADCC directed against tumor targets. However, when CaP-NPs were properly dissolved by sonication, they did not cause substantial activation. CaP-NPs with SiO2-SH-shell induced some activation of NK cells that was not observed with polyethyleneimine-coated CaP-NPs. Addition of CaP-NPs to NK killing assays did not impair conjugation of NK with OC and subsequent tumor cytolytic NK degranulation. Therapeutic antibody coupled to functionalized CaP-NPs maintained substantial levels of antibody-dependent cellular cytotoxic activity. Our study provides a cell biological basis for the application of functionalized CaP-NPs in immunologic anti-cancer therapies.

Signal Processing Platform for Long-Range Multi-Spectral Electro-Optical Systems.

In this paper, we present a hardware and software platform for signal processing (SPP) in long-range, multi-spectral, electro-optical systems (MSEOS). Such systems integrate various cameras such as lowlight color, medium or long-wave-infrared thermal and short-wave-infrared cameras together with other sensors such as laser range finders, radars, GPS receivers, etc. on rotational pan-tilt positioner platforms. An SPP is designed with the main goal to control all components of an MSEOS and execute complex signal processing algorithms such as video stabilization, artificial intelligence-based target detection, target tracking, video enhancement, target illumination, multi-sensory image fusion, etc. Such algorithms might be very computationally demanding, so an SPP enables them to run by splitting processing tasks between a field-programmable gate array (FPGA) unit, a multicore microprocessor (MCuP) and a graphic processing unit (GPU). Additionally, multiple SPPs can be linked together via an internal Gbps Ethernet-based network to balance the processing load. A detailed description of the SPP system and experimental results of workloads for typical algorithms on demonstrational MSEOS are given. Finally, we give remarks regarding upgrading SPPs as novel FPGAs, MCuPs and GPUs become available.

Robust nonlinear model predictive control based on nominal predictions with piecewise constant references and bounded disturbances

AbstractThis article presents a robust nonlinear model predictive control (NMPC) strategy for constrained systems with piecewise constant references and bounded disturbances based on nominal predictions. An artificial target is employed to avoid feasibility loss due to target change and to provide a potentially enlarged domain of attraction. Recursive feasibility and input‐to‐state stability are ensured. Recursive feasibility is guaranteed due to the tighter constraints that are recursively computed offline based on the disturbance reachable sets. The zonotopic approach from the mean‐value theorem extension is applied to compute these tighter constraints. In the presence of stochastic disturbances with known distribution, individual chance constraints can be considered in the proposed NMPC algorithm. Furthermore, simplified stabilizing ingredients are proposed in order that the robust assumptions hold based on standard algorithms. Case studies based on the continuous stirred tank reactor and buck‐boost control benchmarks are presented to illustrate the main properties of the proposed approach.

Selective Disruption of Survivin's Protein-Protein Interactions: A Supramolecular Approach Based on Guanidiniocarbonylpyrrole.

Targeting specific protein binding sites to interfere with protein‐protein interactions (PPIs) is crucial for the rational modulation of biologically relevant processes. Survivin, which is highly overexpressed in most cancer cells and considered to be a key player of carcinogenesis, features two functionally relevant binding sites. Here, we demonstrate selective disruption of the Survivin/Histone H3 or the Survivin/Crm1 interaction using a supramolecular approach. By rational design we identified two structurally related ligands (LNES and LHIS ), capable of selectively inhibiting these PPIs, leading to a reduction in cancer cell proliferation.

Automatic Extraction of Damaged Houses by Earthquake Based on Improved YOLOv5: A Case Study in Yangbi

Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable.

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

Simulation of chromatic and achromatic assessments for camouflage textiles and combat background

Chromatic and achromatic (AC) assessments of camouflage textiles have been critical to the defense researchers for concealment, detection, recognition, and identification (CDRI) of target signature against multidimensional combat background (CB). AC assessment and camouflage measurement techniques are simulated and experimented for assessment of camouflage textiles against CB. This model has been demonstrated for color measurement spectrophotometer, scanning electron microscopy (SEM), digital imaging, hyperspectral imaging, and image processing software (ImageJ) for the advancement and establishment of AC camouflage textiles assessment. The chromatic variations of 48 artificial target objects (TOBs) have been synthesized by image processing; the technique can be implemented for defense CB-CDRI assessment. Microstructural variation versus optical signal of woodland, desertland and stoneland CB materials have been elucidated by SEM magnification. The achromatic variation of CB materials have been demonstrated for the replacement of optical signal against modern remote sensing device to the imaging sensor. Color difference (Δ E), microstructural variations, pixel variations to imaging signal and standard deviation of CB materials have been represented for remote sensing surveillance of defense applications against TOB-CB-CDRI. Technical simulation of color, texture, gloss, and pixel intensity has been derived for AC-CDRI assessment of camouflage textiles in TOBs-CB environment.

Multiple Natural Features Fusion for On-Site Calibration of LiDAR Boresight Angle Misalignment

Boresight angle misalignment is a major error source in a mobile LiDAR system (MLS), which directly affects the overall accuracy and quality of MLS scanned point clouds data. However, the current calibration of the boresight angle misalignment mainly relies on artificial target features or a manual adjustment, and the intensive labors dramatically limit the calibration flexibility. To solve these problems, this paper develops a novel on-site calibration method for boresight angle misalignment based on multiple natural features constraints, which can automatically incorporate multiple natural features extracted from surrounding environments to generate more accurate calibration results for MLS boresight angle without used any artificial targets or specific facilities. First of all, an improved 4-points congruent sets (I-4PCS) algorithm is proposed for registering the MLS point clouds in forward and backward scanned overlapping areas and realizing smooth global registration for point clouds data. Secondly, a weight principal component analysis (WPCA) approach is presented to automatically extract the appropriate multiple natural features from the well registered point clouds and establish the appropriate features representation. Thirdly, according to the extracted multiple features, the certain geometric constrains equations for spherical, linear/cylindrical, planar features are established based on a model adjustment strategy. Lastly, the boresight angle misalignment calibration can be achieved through fitting the corresponding geometric constrains equations and minimizing the weighted through a least-squares adjustment process. The experimental results demonstrate that the proposed method can effectively on-site calibrate the boresight angle misalignment error, and the overall performance of MLS is significantly improved after the calibration based on multiple natural features constraints.

Phase Error Analysis and Compensation of GEO-Satellite-Based GNSS-R Deformation Retrieval

Beidou geostationary earth orbit (GEO) satellite-based global navigation satellite system reflectometry (GNSS-R) technique has been developed for measuring surface deformation in a carrier-phase-based, cost-effective, and continuous manner. However, several improper assumptions in the original system and method, such as neglecting the slight GEO satellite movement during angle determination and deeming inter-channel phase difference as time-invariant, can introduce certain errors in estimating the surface deformation. In this work, these issues were analyzed and improved for more accurate estimations. Utilizing a Wilkinson power divider, the inter-channel phase error is estimated and calibrated with direct signal in the master channel. The GEO-motion phase error is theoretically modelled and compensated for based on precise ephemeris. A compensating algorithm of GNSS-R deformation retrieval technique utilizing Beidou GEO satellites is presented, by which encouraging results are yielded from field experiments emulating long-term monitoring. On artificial targets, the retrieval root mean square (RMSE) is better than 8 mm.

Artificial Intelligence Technologies for Customs Control

The article presents the results of a study of promising areas for the use of artificial intelligence technologies for customs control, based on an analysis of the possibilities of artificial intelligence technologies and targets for the activities of the customs authorities of Russia until 2030. The directions of actual control, documentary control and classification of goods are considered. During actual control, it is proposed to use artificial intelligence at intelligent checkpoints, along with the use of artificial intelligence technologies for recognizing images obtained with the help of inspection and screening complexes, which will be interfaced with a system of sensors, which will automatically receive the necessary information about goods and vehicles crossing the customs border. In documentary control, artificial intelligence technologies can be used to automatically check documents submitted in printed or handwritten form, including those in foreign languages. Artificial intelligence technologies based on algorithms for the semantic analysis of unstructured information will be useful for automatic classification of goods in accordance with the unified Commodity Nomenclature for Foreign Economic Activity of the Eurasian Economic Union.

Stereo Vision Controlled Variable Rate Sprayer for Specialty Crops: Part I. Controller Development

Highlights Developed a stereo vision spray controller with a single board computer for precision sprayers. Developed algorithms to process stereo vision data, calculate spray volumes, and activate spray nozzles. Developed a graphical user interface to embed algorithms and operate the controller. Controller detected target volume and sprayed target in real time at various travel speeds before target was on spray area. The spray controller consumed 111 to 141 ms to acquire and process data and determine spray output. Abstract. Under laboratory conditions, a variable rate spray controller using stereo vision as a target detection sensor was developed and evaluated with moving spray targets at travel speeds ranging from 3.2 to 8.0 km h-1. The controller detected the target with a resolution of 480 pixels (vertical) × 640 pixels (horizontal) at a frame rate of 5 Hz and modulated spray nozzles at 10 Hz based on the analysis results of the stereo vision data. A customized graphical user interface was developed to operate the controller, display incoming data, and control other components. Evaluations of the controller with a flat, 3-dimensional box column and artificial tree targets with travel speeds ranging from 3.2 to 8.0 km h-1 showed that the controller consistently activated the nozzles 158.7 to 293.6 ms before the spray target arrived on the spray axis. The controller was also capable of repeatedly detecting the volume of the spray target with variations less than 12% for the same target at one travel speed, although it measured the volume generally higher than the manually calculated volume by as much as 33%, which resulted from duplicate target detection between detection cycles of the controller. These duplicated detections caused the overapplications of as much as 38%, although the controller repeatedly discharged spray volumes with variations less than 11% for the same target at the same travel speed. On average, the spray controller required 111.2 to 140.7 ms for data processing in one detection cycle, from image acquisition to activating spray nozzles for travel speeds ranging from 3.2 to 8.0 km h-1, with the potential to reduce this further. The developed controller would provide an economical and versatile platform for future variable rate sprayers with the benefits of stereo vision technology. Keywords: Automation, Crop protection, Precision Agriculture, Pesticide, RealSenseTM.

The Influence of the Azimuth RCS Pattern of Calibrator on SAR Absolute Calibration

The realization of radiometric calibration relies on the known artificial point targets as the reference, such as corner reflectors. One of the criteria of the calibrator is that the 3 dB-beamwidth of the azimuth radar cross section (RCS) pattern needs to be larger than the synthetic aperture radar (SAR) antenna 3-dB beamwidth to ensure the SAR antenna, which has a narrow azimuth beamwidth, can align to the calibrator while also making the nominal RCS value of the calibrator approximately constant during the radiometric calibration. However, the RCS of the calibrator is actually inconstant within a synthetic aperture period, especially when the SAR antenna beamwidth is wide. The simplified treatment will introduce errors to calibration. In this letter, the azimuth RCS pattern is taken into account. The error caused by the constant RCS is modeled. Based on the error model, an improved criterion of the calibrator is discussed. The experiments use the known SAR system parameters for numerical simulation based on dihedral corner reflectors (DCRs) with different sizes. The results verify the error model and indicate that when the SAR beamwidth is larger than the beamwidth of the calibrator, and the introduced error cannot be ignored. The relationship between the size of the calibrator and the SAR beamwidth is further analyzed through the experiments.

Global Registration of Multiview Unordered Forest Point Clouds Guided by Common Subgraphs

To register multiview, unordered point clouds from forest scenes, we must establish how scans are associated. The proposed method, called RegisMUF, can register arbitrary forest point clouds from aiming scenarios without knowledge of initial position and orientation, without requiring artificial targets, and without recording the order of the scanning sequents. One of the novel contributions of the proposed method is the optimization of a scanning network. We exploit common subgraphs to connect data between spatial subsets and subsequently predict the overlapping areas between adjacent scenarios. In parallel, we propose a rapid coarse strategy and an accurate tree-oriented refinement strategy. Finally, all the scenarios converge to an anchor by combining the minimum loop expansion approach and a parallel merging approach. We experimentally evaluate three challenging data sets: one data set from the FGI benchmark Evo, Finland with five scans, and two other forest data sets from Jiangxi province, China, with 15 and 23 scans. For each data set, the network-building module of RegisMUF produced 100% correct connections to associate all the scans. Together, 108 pairwise cases are exploited to evaluate the matching module in RegisMUF, and the results reveal that the proposed method is superior to or on par with state-of-the-art practices in terms of registration accuracy and successful-registration rate. In end-to-end tests, RegisMUF performs impressively both in terms of registration accuracy and computational cost.

Systematic end-to-end testing of multiple target treatments using the modular RUBY phantom

The RUBY head phantom in combination with the System QA insert MultiMet can be used for simultaneous point dose measurements at an isocentric and two off-axis positions. This study investigates the suitability of the system for systematic integral end-to-end testing of single-isocenter multiple target stereotactic treatments. Several volumetric modulated arc therapy plans were optimized on a planning CT of the phantom positioned in a stereotactic mask on the stereotactic treatment board. The plans were created for three artificial spherical target volumes centred around the measurement positions in the MultiMet insert. Target diameters between 5 and 40 mm were investigated. Coplanar and non-coplanar plans were optimized using the collapsed cone algorithm of the Oncentra Masterplan treatment planning system and recalculated with the Monte Carlo algorithm of the Monaco treatment planning system. Measurements were performed at an Elekta Synergy linear accelerator. The head phantom was positioned according to clinical workflow comprising immobilization and CBCT imaging. Simultaneous point dose measurements at all target positions were performed with three PinPoint 3D chambers (type 31022) as well as three microDiamond detectors (type 60019) and compared to the treatment planning system calculations. Furthermore, the angular dependence of the detector response was investigated to estimate the associated impact on the measured point dose values. Considering all investigated plans, PTV diameters and positions, the point doses calculated with the Monaco treatment planning system and the microDiamond measurements differed within 3.5%, whereas the PinPoint 3D showed differences of up to 6.9%. Point dose differences determined in comparison to the Oncentra Masterplan dose calculations were larger. The RUBY system was shown to be suitable for end-to-end testing of complex treatment scenarios such as single-isocenter multiple target plans.

Enhancing community creativity through training on making artificial flowers from plastic waste

The COVID-19 pandemic has a substantial impact on the community, one of which is in the economic sector. Many workers have been laid off, leading to a rise in the unemployment rate in Indonesia. This community service activity aims to help people affected by layoffs as the result of the COVID-19 pandemic through the activities of providing and strengthening functional skills to the community. This online program is a form of the Thematic Community Service Program on Prevention and Countermeasure of The COVID-19 Impact. An artificial flower-making training program has been implemented from plastic waste into a work that can increase people's creativity and grow the community's economy. This artificial flower-making training program's target is based on plastic waste in the community from RT/ RW 01/01, Bojong Village, Majalaya, Bandung District, West Java Province. This program was attended by eight people conducted online through WhatsApp Group from December 1 to 10, 2020. In addition to helping the community increase creativity during the COVID-19 pandemic, creating economically valuable communities can indirectly reduce plastic waste in landfills because plastic waste cannot decompose naturally. People who follow this program can sell artificial flowers to improve the economy during the COVID-19 pandemic.

A Novel Extrinsic Calibration Method of Mobile Manipulator Camera and 2D-LiDAR via Arbitrary Trihedron-Based Reconstruction

Mobile manipulators are increasingly applied to improve the efficiency in industrial manufacturing. As a typical system using multi-sensor fusion technology, accurate extrinsic calibration of manipulator’s exteroceptive sensors like camera and 2D-LiDAR is essential for mobile manipulators to perform complicated task such as mobile assembly. However, most existing camera-LiDAR calibration methods require sophisticated artificial calibration targets, leading to implementation restrictions. In this paper, by reconstructing an arbitrary trihedron that existed in the general human-made environment, a novel method is presented to estimate the transformation between the manipulator camera and the 2D-LiDAR coordinate system. The proposed method is based on the use of point, line, and plane geometry constraints between the segmented 2D-LiDAR scan and the reconstructed trihedron features. Considering the metric of reconstruction, the extended hand-eye calibration framework is implemented to recover the scale factor and hand-eye parameters. Then, a new optimization model is presented to reconstruct the key features of arbitrary trihedron in a preset global coordinate system. Finally, with only one 2D-LiDAR measurement, camera-LiDAR transformation can be calculated by constructing point-to-line and line-to-plane geometric constraints. Further, the transformation between the manipulator kinematic base and 2D-LiDAR can also be calibrated. Both simulation and real-world experiments show that the proposed method can provide robust and accurate results.