Precise Results Research Articles

Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.

FRACTIONAL ANALYSIS OF ONE-DIMENSIONAL SCHRÖDINGER MODEL IN THE CONTEXT OF CAPUTO-TYPE FRACTIONAL DERIVATIVES BASED ON RESIDUAL POWER SERIES METHOD

This research presents a novel analytical approach to explore the fractional analysis of the one-dimensional time-fractional Schrödinger model (TFSM) using Caputo fractional derivatives. By integrating the Mohand transform (MT) with the residual power series method (RPSM), we develop the Mohand residual power series method (MT-RPSM) that provides results in the form of convergent series without assumptions on variables. Initially, we employ MT to reduce the fractional order, and then we transfer the fractional problem into the Mohand space formulation. Second, we use the RPSM concept to derive the iterative series formula for the Mohand space formulation. We analyze these findings using visual layouts to show the physical representation of the TFSM, which matches the precise results very well. The results indicate that the proposed technique is a reliable and practical method for identifying and analyzing various nonlinear models of physical phenomena.

Advanced Prediction Models for Scouring Around Bridge Abutments: A Comparative Study of Empirical and AI Techniques

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

Analytical investigation of porous medium effects on the flow of two micropolar fluids in a rotating annulus

Fluids with microstructures and microinertia play a vital role in microfluidics and nanfluidics system. One of such significant fluid is micropolar fluid. The characteristic behavior of micropolar fluid flow in conduit/pipe, etc. filled with porous medium helps us understand the flow behavior of biological fluids in porous media, groundwater flow in aquifiers, rotatory machines or viscometer. To understand such real-world problems, it is necessary to get into the mathematical model of such flow models. One such model with wide number of application is presented in this paper. This work investigated the flow of two micropolar fluids between the region formed by two concentric cylinders. Authors have considered inner cylinder to be at rest and outer cylinder rotating at a constant angular velocity. The region between two concentric cylinder is filled with an isotropic porous material. A slip condition at the outer wall and continuity conditions at interface of two fluids has been utilized to obtain an analytical solution for the proposed model. The numerical solution of the obtained solution for present problem is used to graphically analyze the motion of immisicble micropolar fluids rotating in an annulus filled with the porous medium. It is found that an outer cylinder has a notable influence on the flow behavior of immiscible micropolar fluids, contrary to the effect observed within the inner region. An analytical approach of this work not only helps us obtain precise results and analysis of the flow, but it also serves as a useful validation for future approximations within the scope of this work.

Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search

This paper proposes an efficient productivity-aware optimization framework that utilizes hybrid machine learning with parallel global search to timely and appropriately adjust the critical control parameters (CCPs) of a cutter suction dredger (CSD) during construction. This optimization framework consists of three main parts. First, a hybrid Jaya–multilayer perceptron (MLP) algorithm was developed to rapidly construct a model that captures the interaction between construction parameters and slurry concentration. Next, the preliminary coarse results for the CCPs are determined through multi-parameter sensitivity analysis. Finally, the proposed resilient-zone parallel global search algorithm was employed to further optimize the CCPs, yielding more precise optimization results. To validate the proposed optimization framework and implement the in-situ service, it is applied to a real-world case study involving “Tianda” CSD construction. The results demonstrated that the average optimization duration is 6.7 s, which is shorter than the data acquisition interval of 8 s. Our approach improves the computational efficiency by 9.4 times compared with traditional optimization control methods. Additionally, there is a significant increase in the slurry concentration, with the maximum growth rate reaching 81.64%.

3D coherent single shot lidar imaging beyond coherence length

Advancements in remote sensing and autonomous vehicle technologies made lidars equally important for unmanned objects alongside cameras. Therefore, precise 3D lidar imaging and point cloud generation have become important subjects. Although existing coherent lidar technologies provide precise imaging results, the spectral linewidth of the laser sources becomes a key limitation over long distances as it defines the maximum detection range. Here, we present long-distance 3D lidar imaging which removes the coherence length limitations and therefore the necessity of high-coherence laser sources. Mainly, we generate optical sidebands, by modulating a continuous wave (CW) laser source with multiple radio-frequency (RF) tones. Then, using our own post-processing and triangulation methods, we use the relative phase changes between the sidebands which are free from laser phase noise to determine the target distance. We prove that the multi-tone coherent Lidar technique can perform precise 3D imaging and point cloud generation of various targets at sub-10pW optical power reception and distances up to ∼12× beyond the coherence length of the CW laser employed in the lidar architecture. Overall, it is demonstrated that coherence length restriction is removed by the suggested method, which makes precise long-distance 3D lidar imaging possible, particularly for applications such as spacecraft and aerial coherent lidars.

Customized Genioplasty and Advantages of 3D Virtual Planning: An Updated Literature Review.

Genioplasty is a surgical technique that modifies the projection of the chin in three dimensions to achieve symmetry and facial harmonization. Virtual 3D planning is increasingly used, supplanting the conventional surgical technique due to the precise and predictable results obtained.The definition of the objective of the study was first carried out using the PICO (Patient, Intervention, Comparison, Outcome) question method. Posteriorly, an updated literature review was carried out in the "PubMed" database using the keywords "Genioplasty and Virtual 3D Planning," obtaining 11 articles for the study with the objective of defining the advantages and disadvantages of performing a virtually planned genioplasty, comparing it with the conventional technique.In the results, it is observed that virtually planned genioplasty presents greater precision, a reduction in surgical time, and a lower rate of complications than the conventional technique. Virtual planning using computer-aided design/computer-aided manufacturing technology presents good reproducibility in the patient with <2 mm variations between what was planned and what was obtained postoperatively, with statistically significant results (p < 0.001).In conclusion, virtually planned genioplasty with customized cutting guides and osteosynthesis plates achieves very precise surgery results with good reproducibility, reducing surgical time, intraoperative complications, and the difficulty of the surgical technique.

Determination of rare earth elements in synthetic calcium phosphates by high-resolution continuum source electrothermal atomic absorption spectrometry

Ceramic, cement and composite biomaterials have been developed based on hydroxyapatites (HA) and tricalcium phosphates (TCP), which are analogous in phase and chemical composition to the mineral component of bone tissue. The crystal structures of HA and TCP are arranged in isomorphic substitutions. Recently, research has focused on the modification of HA and TCP structures with ions of various metals, including rare earth ions (REEs), with the aim of creating materials with a range of beneficial properties for medical applications. REEs are known to have a number of useful properties, including antibacterial, antitumour, catalytic, magnetic and luminescent properties. The replacement of some of the Ca ions in the structures of HA and TCP with REE ions therefore makes it possible to obtain a material with biocompatibility and biological activity, giving it the required properties depending on the REE used and its concentration. In order to achieve the specified properties, it is necessary to control not only the structure (phase composition, lattice parameters of the powders) and the presence of characteristic functional groups, but also the chemical elemental composition. Modifications of hydroxyapatites and tricalcium phosphates containing from one to several different alloying elements are currently being developed. Various analytical methods are used for this purpose, including X-ray, atomic emission and a number of others. This article is devoted to the study of the analytical capabilities of the method of atomic absorption spectrometry with electrothermal atomization and a continuous spectrum source in relation to the determination of Eu and Yb in hydroxyapatites and tricalcium phosphates. The article considers the optimal conditions and modes of analysis, including temperature-time programs, the use of modifiers, the construction of calibration curves, and other factors that can be adjusted for more precise results. The results demonstrated the possibility of simultaneous determination of both Eu and Yb in the concentration range of 0.09 to 2 wt.%, with a relative standard deviation of less than 6 rel.%.

Analysis of ultrasonic device influence on the ultrasonic thickness measurement of the steel grade

ABSTRACT Ultrasonic thickness measurement depends on the capability of the equipment and its operating characteristics, as well as on the auxiliary means used to adjust the system and analyse the obtained results. The characteristics of the ultrasonic device commonly used as a flaw detector are crucial for achieving accurate and precise results and are very important for the reliability of the results obtained in the measurement process, especially in production systems. This article aims to determine the quantitative variations in thickness caused by influencing parameters of the ultrasonic device and chosen ultrasonic probe, their quantification of interactions, and the significance of their influence on the accuracy and precision of the results. In the experimental part of the research, a factorial plan of the experiment was carried out to determine the influence of the ultrasonic device and the type of ultrasonic probe on the variability of the results of the ultrasonic thickness measurement. The parameters of the ultrasonic device and probes were analysed, and the experimental plan for developing the regression model was elaborated. The response surface methodology was applied, and the in-service measurement process was simulated using Monte Carlo simulation.

SUITABILITY MAPPING FOR OPTIMAL LANDFILL SITE IN COASTAL MOUNTAIN MUNICIPALITIES. CASE STUDY: COLLO MASSIF (NORTHEASTERN ALGERIA)

The management of Municipal Solid Waste (MSW) in coastal mountainous regions displays a significant challenge to sustainable development in Algeria, particularly in its environmental dimension. Local stakeholders face substantial difficulties in identifying appropriate sites for solid waste disposal in this area due to the fragility and complexity of their ecological systems and their potential to amplify the impacts of waste disposal. This affects directly the quality of life, sustainability of natural resources, and threatens the scenic beauty of this tourist destination. Therefore, developing a mechanism based on scientific knowledge to facilitate decision-making processes in these regions is imperative. This paper aims to underscore the importance of employing Cartographic Suitability Mapping for Optimal Landfill Site Selection in coastal mountainous regions as an effective tool to ensure precise results, focusing on the study case of Collo, Skikda state, Algeria. The results show four categories of land suitability: constraint (5.22-7.76) , unsuitable (3.25-4.26), and suitable area (1.21-3.24), with four candidate areas identified as the most suitable for landfill establishment. It is recommended that future studies incorporate social and economic dimensions to further enhance decision-making processes in this regard.

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

This study tackles image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches have high computational costs and may not capture sufficient high-level contextual information, such as spatial structures or semantic shapes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents semantic structures. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Notably, our coarse-level matching network enhances efficiency by employing attention layers only to fixed-sized topics and small-sized features. Finally, we design a dynamic feature refinement network for precise results at a finer matching stage. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method ranks in the top 9% in the Image Matching Challenge 2023 without using ensemble techniques. Additionally, we achieve an approximately 50% reduction in computational costs compared to other Transformer-based methods. Code is available at https://github.com/TruongKhang/TopicFM.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

Developing a machine learning-based methodology for optimal hyperparameter determination—A mathematical modeling of high-pressure and high-temperature drilling fluid behavior

Drilling fluids exhibit complex rheological behavior due to a non-linear response to shear rate variations and high sensitivity to changes in temperature, time, and pressure conditions. The prediction of drilling fluid rheological behavior is crucial for the success of oil well drilling, and it directly impacts the fluid's performance. The dataset used in this study was obtained from extensive rheometric tests of water-based and olefin-based drilling fluids in steady-state flow curves. The optimal hyperparameters were guided by performance metrics and compared with alternative models such as Power-law and Herschel-Bulkley rheological models. Different configurations with different hidden layers, using neuron sequences of 16, 32, and 64, learning rates of 0.001 and 0.01, and the ReLU activation function were used to improve the model's performance. Additionally, the paper delved into the impact of the number of training epochs on the accuracy of shear stress predictions. Finding this equilibrium was identified as a crucial factor in achieving precise results. The neural network model demonstrated remarkable accuracy when using the ML-C3 configuration, with MAE values of 0.535 and R2 of 0.987 in predicting the steady-state flow curves of drilling fluids, establishing itself as a powerful tool for forecasting the rheological behavior of these fluids under diverse operational conditions. The present research significantly contributes to the field of drilling fluid rheology and provides valuable insights for optimizing drilling operations in HPHT environments.

Green Computing Innovation Index Measurement Using Fuzzy Inference System for Young Inventors

Green Computing Innovation (GCI) aims to address the negative effects of traditional computing on the environment and promote sustainable practices in computing. Subsequently, the Green Computing Innovation Index (GCII) was used to measure young inventors' inventiveness to evaluate from green computing perspectives. Nonetheless, one of the most significant challenges evaluators face in the measurement process includes uncertainty and imprecise data in measuring the young inventors' green computing innovation. Thus, this paper measures the Green Computing Innovation Index (GCII) with a novel approach utilizing Fuzzy Inference System (FIS) with the Mamdani method. The study measures the top 10 young inventors' innovations from the Faculty of Computing and Meta-Technology, UPSI. Fuzzy Inference System will assist in providing improved decision-making solutions for evaluators to prevent any uncertainty and subjectivity during the evaluation process. The development of FIS is based on four key innovation elements. It uses a triangular membership function with Centroid, Middle of Maximum (MOM), Smallest of Maximum (SOM), Bisector and Largest of Maximum (LOM) methods for defuzzification. The centroid defuzzification method is observed to perform better than other defuzzification methods. It is due to the fact that the adopted defuzzification approach is equally sensitive to all key innovation criteria inputs and can truly represent the minor change in the values of any parameters across all products. The centroid method was found to be the most efficient for decision-making, providing precise results with the ability to detect discrepancies as small as 0.001. Thus, the centroid method can improve decision-making efficiency, leading to fairness in selecting the best green computing innovation product. Ultimately, GCII rating contribute mainly on encouraging young inventors to innovate and promote sustainable practices in innovations.

Psychometric Properties of the Chinese Version of the Gilliam Autism Rating Scale-Third Edition (CV-GARS-3).

Gilliam Autism Rating Scale-Third Edition (GARS-3) serves as an effective screening tool for autism spectrum disorder (ASD) that is based on the latest and authoritative diagnostic criteria, however, there is a deficiency in adaptive research in China. We aimed to revise the Chinese version of GARS-3 (CV-GARS-3) and evaluate its psychometric characteristics, providing a theoretical basis for the improvement of ASD screening tools in China. This study developed CV-GARS-3 through translation and cultural adaptation of GARS-3. 362 ASD individuals, 126 typical development individuals, and 103 individuals with other disorders were recruited to analyze the psychometric characteristics of CV-GARS-3. The results showed that exploratory structural equation model demonstrated satisfactory goodness-of-fit. Within the non-verbal ASD samples, all items loaded on anticipated factors. Regarding verbal ASD samples, 3 items exhibited considerable cross-loadings and were categorized under unexpected factors. Meanwhile, acceptable criterion validity was reflected in the four subscales (r = 0.71) and the six subscales (r = 0.74). Satisfactory reliability was observed in the four subscales (Cronbach's α = 0.96, inter-rater consistency = 0.86, test-retest consistency = 0.87) and the six subscales (Cronbach's α = 0.94, inter-rater consistency = 0.81, test-retest consistency = 0.81). In addition, receiver operating characteristic analysis demonstrated that both the four subscales (sensitivity = 89%, specificity = 85%, accuracy = 88%) and the six subscales (sensitivity = 86%, specificity = 88%, accuracy = 86%) had outstanding screening effects. Therefore, the results suggested that the CV-GARS-3 is considered as a useful tool for the screening and auxiliary diagnosis of ASD. Notably, the expression of scale should be further improved to adapt the context of Chinese culture and achieve more precise diagnostic results.

Intaglio surface of CNC milled versus 3D printed maxillary complete denture bases – An in vitro investigation of the accuracy of seven systems

ObjectivesThe accuracy of the intaglio surface of maxillary complete dentures produced with a variety of digital manufacturing systems have been comprehensively investigated, however, not for multiple systems in the same study. This in vitro study endeavors to measure and compare the accuracy of the intaglio surface of maxillary complete denture bases constructed with different photopolymerization-based 3D printing and CNC milling systems. Materials and MethodsTwo subtractive manufacturing machines and five additive manufacturing machines were used to manufacture a maxillary denture bases (n = 10) for analysis. The samples were stored in artificial saliva at 37 °C for 7 days to mimic intraoral use before they were air dried for 5 min, and then scanned with an E3 dental laboratory scanner to generate STL files. A 3D metrology software program was used to analyze the data against the original CAD design. Statistical differences were determined with 1-way analysis of variance (ANOVA) and the Kruskal Wallis test (α=0.05). Color deviation heat maps were used to interpret areas of clinical significance. ResultsThe results indicated that the subtractive manufacturing method delivers the truest and most precise intaglio surface of maxillary complete denture bases. The additive manufacturing technique delivers less true and less precise results with inconsistency observed between the different systems as opposed to the subtractive manufacturing groups which showed no significant differences. ConclusionsWhen manufacturing a complete maxillary denture base subtractive manufacturing will deliver the most consistent and true results.

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

The Versatility of the Taguchi Method: Optimizing Experiments Across Diverse Disciplines

AbstractThe Taguchi method, a robust experimental design technique, establishes a strong connection between input and output variables. Known for its capacity to yield precise results with fewer trials and minimized errors, this method has gained widespread application in various fields such as engineering, physics, chemistry, economics, finance, and more. In this paper, the authors examine the importance of the Taguchi orthogonal array method, its step-by-step optimization procedure, and its potential for future applications. Through a thorough literature review, the authors investigate how the Taguchi method has been effectively employed to identify key factors influencing response variables. The versatility of the Taguchi method becomes apparent when considering its applications across diverse disciplines. Researchers in engineering have successfully utilized this technique to optimize processes and enhance product quality. Furthermore, in scientific fields like physics and chemistry, the Taguchi method has proven invaluable for conducting experiments efficiently, resulting in more accurate and reproducible outcomes. Researchers gain critical insights into the effects of factors on the response variable by employing statistical tools such as mean analysis, variance analysis, and signal-to-noise ratio. The Taguchi method remains a valuable and broadly applicable tool for optimizing experiments and identifying influential factors across multiple disciplines. This paper’s extensive literature review emphasizes its significance in various fields and outlines the step-by-step procedure to leverage its potential for optimization.

Laser-inertial tightly coupled SLAM system for indoor degraded environments

Purpose To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results. Design/methodology/approach The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification. Findings A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments. Originality/value This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

Satellite-based Spectral Indices for Extracting Village Water Bodies in Ludhiana District of Punjab, India

Ensuring water security is the foremost priority of every nation, and water bodies are the most precious life-sustaining resources on the earth. Availability of reliable and updated information of water bodies including knowledge about their locations is necessary for the sustainable planning and management of water resources at a regional scale. Remote sensing technique has revolutionized extraction of village water bodies as it provides fine spatial resolution, gives rapid and precise results and covers large areas, and hence, it has been widely used in the recent studies. In this study, three remote sensing-based spectral indices, i.e., normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and normalized difference pond index (NDPI) were employed to demarcate the area of ponds in each village of Ludhiana district in Punjab. Accuracy of the spectral indices was evaluated by comparing the area of the ponds delineated by the indices with that of digitized manually. The results indicated that NDWI, MNDWI and NDPI could identify 370, 1263 and 1410 number of village ponds, respectively. On the other hand, a total of 1513 village ponds could be demarcated through manual digitization. Thus, the efficiency of NDWI in identification of correct number of village ponds was 24.5%, while the efficiency of MNDWI and NDPI was 83.5% and 93.2%, respectively. Furthermore, NDPI demonstrated an efficiency of 60-65% in demarcating the area of village ponds.