Scene Simulation Platform Based on Data Fusion of Multiple Format 3D Models

Real-time, low-resource corridor reconstruction using a single consumer grade RGB camera is a powerful tool for allowing a fast, inexpensive solution to indoor mobility of a visually impaired person or a robot. The perspective and known geometry of a corridor is used to extract the important features of the image and create a 3D model from a single image. Multiple 3D models can be combined to increase confidence and provide a global 3D model. This paper presents our results on 3D corridor modeling using single images. First a simple but effective 3D corridor modeling approach is introduced which makes very few assumptions of the camera information. Second, a perspective based Hough transform algorithm is proposed to detect vertical lines in order to determine the edges of the corridor. Finally, issues in real-time implementation on a smartphone are discussed. Experimental results are provided to validate the proposed approach. This work has the potential to also function in environments with properties analogous to corridors such as highways, sidewalks, city blocks, etc.

Abstract. The Project “Beyond East and West: Geocommunicating the Sacred Landscapes of “Duklja” and “Raška” through Space and Time (11th–14th Cent.)” attempts to recreate and communicate the sacred landscape during a time of transition and transformation. The project has an interdisciplinary approach and incorporates multiple media, like maps, images, and 3D models. The study area of the project is situated at the junction between the Dinaric mountain range and the coastal region of the Adriatic Sea. Historically, the region was shaped by the power struggle between Byzantium, the First Bulgarian Empire, and the Serbian Realm. Ecclesiastically, it was a zone of interaction as well as encounter between Rome and Constantinople. The aim of the project is to discover and visualise the spatial and temporal aspects of these encounters and transformation processes.To communicate the historical sacred landscape, a map-centred online application is used as a hub. To explore the relations between places, events, actors and artifacts, the users can follow the links between the entities. Content created during this project is more than points, lines or polygons which can be displayed on a map. Many objects are complemented with images, and for selected churches 3D models using aerial images captured by UAVs will be created. “Story Maps” are created for selected core research topics to provide an easily accessible starting point for users. The broader aim beyond the current research project is to provide a flexible framework, which can serve as a platform for similar research projects in historical geography and digital humanities.

Recovering a complete, detailed, accurate and realistic 3D model from images is still a difficult task. This paper proposes an improved 3D modeling using multistage feature extraction and matching. A 3D modeling technique is applied based on the features extracted from images of the same scene taken from different directions. Matching of the images are used to construct the 3D model. Using multistage features extraction and matching algorithms, multiple 3D models can be obtained. This paper will focus on modeling from reality rather than computer graphics creation of artificial world models. Experimental results are illustrated to explore the effectiveness of the proposed method.

Litopenaeus vannamei (Whiteleg shrimp) is a crucial crustacean species in global aquaculture, but infectious disease outbreaks, particularly from pathogens like Vibrio harveyi, threaten its sustainable growth. Understanding shrimp-pathogen interactions, particularly through Toll-like receptors (TLRs), is essential for improving disease resistance. This study identifies and characterizes TLR6 and TLR13 in Whiteleg shrimp through genome-wide analysis. Using bioinformatics tools, we analyzed their physiological properties, domain structures, multiple sequence alignments, phylogeny, and 3D models. Noticeably, TLR6 and TLR13 contain open reading frames (ORF) encoding 1349 and 1092 amino acids, respectively, with typical leucine-rich repeats, transmembrane, and toll-interleukin-1 receptor (TIR) domains. Multiple sequence alignment indicated the conservation of sequence in the leucine-rich repeat domain. Phylogenetic analysis showed that TLR6 and TLR13 are closely related to Penaeus japonicus and Penaeus monodon. Quantitative PCR (q-PCR) revealed TLR6 had higher expression in hemocytes and heart, while TLR13 was most expressed in the intestine. After Vibrio harveyi infection, TLR6 was upregulated in the gills, though TLR13 showed no significant change in the gills or hepatopancreas. These findings provide new insights into the immune function of whiteleg shrimp`s TLR6 and TLR13 against V. harveyi.

To improve the maintainability, scalability, and reusability of models in complicated Missile Weapon System simulation, the system of a hardware-in-the-loop (HIL) simulation system based on high Level Architecture (HLA) and Modelica is designed, which can satisfy the need of interoperability among federates, and the simulation model library of missile based on Modelica is developed. Simulation application of missile trajectory and scene simulation is devised and is introduced into the federation to achieve the interaction between them and other federates. New simulation application incorporated with latest simulation technology can easily be introduced into the existing simulation system if they are in accordance with the HLA standards. The scene simulation application provides developers with a visual method to assess the performance of the system, which works as a federation in the HLA-based system. The results of missile trajectory simulation and scene simulation are given. The reusability of Modelica-based missile model provides designers with great flexibility and reusability in developing new missile models.

A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales

There exist quite a few criteria for penalty-based model selection. Although they have various justifications for large sample problems, their performance under small or moderate sample size is unclear which hinders the development of model combination methods using the appropriate penalty term. In this paper, we assess the performance of seven model selection criteria based on linear regression models with unknown noise variance. We set the true data generation mechanism to be within the model set as well as outside the model set. In the latter case, soft model selection through multiple model fusion is proposed and its difference from Bayesian model averaging is highlighted. The penalty term used in each model selection criterion provides a natural link to estimate the model probability without assuming any prior knowledge of the unknown parameter. An important question is whether the estimated model probabilities are consistent when multiple models are fused for prediction or interpolation. We argue that strong consistency only holds under large sample regime while soft model selection can still be better than choosing a single model with small sample size. Our numerical results using different model selection criteria for polynomial fitting indicate that the conditional model estimator (CME) has the best performance in selecting the correct model order and fusing multiple models for prediction and interpolation. The minimum description length (MDL) based criteria are next to CME and outperform Bayesian information criterion (BIC) and Akaike information criterion (AIC) significantly.

Modelling the decadal trend of ecosystem carbon fluxes demonstrates the important role of functional changes in a temperate deciduous forest

An adaptive channel estimation scheme, exploiting the over-sampled complex exponential basis expansion model (CE-BEM), is presented for doubly-selective channels where we track the BEM coefficients via a multiple model approach. In the past work the number of BEM coefficients used to model the doubly-selective channels for channel estimation has been based on an upperbound on the channel Doppler spread. Higher the Doppler spread, more the number of BEM coefficients leading to higher channel estimation variance. In this paper we propose to use a multiple model framework where several candidate Doppler spread values are used to cover the range from zero to an upperbound, leading to multiple CE-BEM channel models, each corresponding to an assumed value of the Doppler spread. Subsequently the well- known interacting multiple model (IMM) algorithm is used for symbol detection based on multiple state-space models corresponding to the multiple estimated channels. A simulation example is presented to illustrate the proposed approach.

A distributed IMM fusion algorithm for multi-platform tracking

Subpixel accuracy image registration is needed for applications such as digital elevation model extraction, change detection, pan-sharpening, and data fusion. In order to achieve this accuracy, the deformation between the two images to be registered is usually modeled by a displacement vector field which can be estimated by measuring rigid local shifts for each pixel in the image. In order to measure subpixel shifts, one uses image resampling. Sampling theory says that, if a continuous signal has been sampled according to the Nyquist criterion, a perfect continuous reconstruction can be obtained from the sampled version. Therefore, a shifted version of a sampled signal can be obtained by interpolation and resampling with a shifted origin. Since only a sampled version of the shifted signal is needed, the reconstruction needs only to be performed for the new positions of the samples, so the whole procedure comes to computing the value of the signal for the new sample positions. In the case of image registration, the similarity between the reference image and the shifted versions of the image to be registered is measured, assuming that the maximum of similarity determines the most likely shift. The image interpolation step is thus performed a high number of times during the similarity optimization procedure. In order to reduce the computation cost, approximate interpolations are performed. Approximate interpolators will introduce errors in the resampled image which may induce errors in the similarity measure and therefore produce errors in the estimated shifts. In this paper, it is shown that the interpolation has a smoothing effect which depends of the applied shift. This means that, in the case of noisy images, the interpolation has a denoising effect, and therefore, it increases the quality of the similarity estimation. Since this blurring is not the same for every shift, the similarity may be low for a null shift (no blurring) and higher for shifts close to half a pixel (strong blurring). This paper presents an analysis of the behavior of the different interpolators and their effects on the similarity measures. This analysis will be done for the two similarity measures: the correlation coefficient and the mutual information. Finally, a strategy to attenuate the interpolation artifacts is proposed

Normalized difference vegetation index (NDVI) carries valuable information related to the photosynthetic activity of vegetation and is essential for monitoring phenological changes and ecosystem dynamics. The medium to high spatial resolution satellite images from Landsat 8 and Sentinel 2 offer opportunities to generate dense NDVI time series at 10-m resolution to improve our understanding of the land surface processes. However, synergistic use of Landsat 8 and Sentinel 2 for generating frequent and consistent NDVI data remains challenging as they have different spatial resolutions and spectral response functions. In this letter, we developed an attentional super resolution convolutional neural network (ASRCNN) for producing 10-m NDVI time series through fusion of Landsat 8 and Sentinel 2 images. We evaluated its performance in two heterogeneous areas. Quantitative assessments indicated that the developed network outperforms five commonly used fusion methods [i.e., enhanced deep convolutional spatiotemporal fusion network (EDCSTFN), super resolution convolutional neural network (SRCNN), spatial and temporal adaptive reflectance fusion model (STARFM), enhanced STARFM (ESTARFM), and flexible spatiotemporal data fusion (FSDAF)]. The influence of the method selection on the fusion accuracy is much greater than that of the fusion strategy in blending Landsat-Sentinel NDVI. Our results illustrate the advantages and potentials of the deep learning approaches on satellite data fusion.

Although multisource sensor (MS) signal-based mechanical fault diagnosis (MFD) can significantly improve the diagnostic performance, the existing methods often lack sufficient adaptability and generalization when retraining on single-sensor signals or inferring from partial sensor signals. Thus, a general two-stage signal representation contrastive learning fault diagnosis framework (T-SCF) is proposed to adapt the trained model to varying numbers of sensor signals. This framework enhances model robustness and data fusion by comparing sensor signal views, offering a new approach for information fusion, fault detection, and classification in MFD. In the first stage, an adaptive contrastive algorithm is proposed to generate contrastive samples (C-Ss) and contrastive labels (C-Ls) for MS signals. Then, a supervised contrastive loss (SCL) is designed to minimize the similarity between different fault MS signals while maximizing the similarity between identical ones. By designing a parallel encoder architecture, SCL enables it to merge contrasting the features of different sensor signals during training. This strategy preserves the time-domain dimension properties of different sensors during the training of the second-stage classifier, thereby improving the adaptability of the model to different sensor signals without affecting the global information. The effectiveness of the method was verified from multiple different evaluation dimensions using two public datasets and one self-built dataset.

Translator Eccentricity Analysis in Tubular Linear Machines using Quasi-3D Finite Element Method Modeling

In this paper, we present a new framework for non-rigid structure from motion (NRSFM) that simultaneously addresses three significant challenges: severe occlusion, perspective camera projection, and large non-linear deformation. We introduce a concept called a model graph, which greatly reduces the computational cost of discovering groups of input images that depict consistent 3D shapes. A 3D model is constructed for each input image by traversing the model graph along multiple evolutionary paths. A compressive shape representation is constructed, which (1) consolidates the multiple 3D models for each image reconstructed during model evolution and (2) reduces the number of models needed to represent the input image set. Assuming feature correspondences are known, we demonstrate our algorithm on both real and synthetic data sets that exemplify all three aforementioned challenges.