Virtual Reality Aided High-Quality 3D Reconstruction by Remote Drones

Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.

Parent grain reconstruction from partially or fully transformed microstructures in MTEX

A versatile generic framework for parent grain reconstruction from fully or partially transformed child microstructures has been integrated into the open-source crystallographic toolbox MTEX. The framework extends traditional parent grain reconstruction, phase transformation and variant analysis to all parent–child crystal symmetry combinations. The inherent versatility of the universally applicable parent grain reconstruction methods and the ability to conduct in-depth variant analysis are showcased via example workflows that can be programmatically modified by users to suit their specific applications. This is highlighted by three applications, namely α′-to-γ reconstruction in a lath martensitic steel, α-to-β reconstruction in a Ti alloy, and a two-step reconstruction from α′ to ɛ to γ in a twinning and transformation-induced plasticity steel. Advanced orientation relationship discovery and analysis options, including variant analysis, are demonstrated via the add-on function library ORTools.

The Warm Winter Paradox in the Pliocene High Latitudes

Reconciling palaeodata with model simulations of the Pliocene climate is essential for understanding a world with atmospheric CO2 concentration near 400 parts per million by volume. Both models and data indicate an amplified warming of the high latitudes during the Pliocene, however terrestrial data suggests Pliocene high latitude temperatures were much higher than can be simulated by models. Here we show that understanding the Pliocene high latitude terrestrial temperatures is particularly difficult for the coldest months, where the temperatures obtained from models and different proxies can vary by more than 20 °C. We refer to this mismatch as the ‘warm winter paradox’. Analysis suggests the warm winter paradox could be due to a number of factors including: model structural uncertainty, proxy data not being strongly constrained by winter temperatures, uncertainties on data reconstruction methods and also that the Pliocene high latitude climate does not have a modern analogue. Refinements to model boundary conditions or proxy dating are unlikely to contribute significantly to the resolution of the warm winter paradox. For the Pliocene, high latitude, terrestrial, summer temperatures, models and different proxies are in good agreement. Those factors which cause uncertainty on winter temperatures are shown to be much less important for the summer. Until some of the uncertainties on winter, high latitude, Pliocene temperatures can be reduced, we suggest a data-model comparison should focus on the summer. This is expected to give more meaningful and accurate results than a data-model comparison which focuses on the annual mean.

Breast Reconstruction–Prospective Follow up on Breast Cancer Patients’ Health-Related Quality of Life

Background Analysing the results of breast reconstruction is important both in terms of oncological safety and health-related quality of life (HRQoL). Immediate breast reconstruction (IBR) is thought to be prone to complications and heavy for patients with no time to adapt to having cancer. Delayed reconstruction (DR) is an option after primary surgery and oncological treatments, but requires patients to go through two recovery periods after surgery. Methods A prospective study of 1065 breast cancer patients with repeated measurement of HRQoL with both generic (15D) and disease specific (EORTC QLQ C-30 BR23) measuring tools included 51 IBR patients and 41 DR patients. These patients’ HRQoL and reconstruction methods were studied in more detail alongside with clinical data to determine HRQoL levels for patients with IBR and those with mastectomy and DR during a 24-month follow-up. Measuring points were baseline, 3, 6, 12 and 24 months. Results Most frequent techniques used were abdominal flaps (IBR n = 16, DR n = 14), latissimus dorsi flaps (LD) (IBR n = 19, DR n = 10), implants (IBR n = 12) and fat grafting (DR n = 6). Smaller groups were excluded from group comparisons. Approximately one third of the patients encountered complications. Symptom scores did not differ between reconstruction methods. DR patients had better overall HRQoL at 12 months, but at 24 months the situation had changed in favour of IBR. Both approaches of reconstructive surgery produced good HRQoL with no significant differences between the approaches studied.

Vehicle Accident Analysis and Reconstruction Methods

In this third edition of Vehicle Accident Analysis & Reconstruction Methods, Raymond M. Brach and R. Matthew Brach have expanded and updated their essential work for professionals in the field of accident reconstruction. Most accidents can be reconstructed effectively using of calculations and investigative and experimental data: the authors present the latest scientific, engineering, and mathematical reconstruction methods, providing a firm scientific foundation for practitioners. Accidents that cannot be reconstructed using the methods in this book are rare. In recent decades, the field of crash reconstruction has been transformed through the use of technology. The advent of event data records (EDRs) on vehicles signaled the era of modern crash reconstruction, which utilizes the same physical evidence that was previously available as well as electronic data that are measured/captured before, during, and after the collision. There is increased demand for more professional and accurate reconstruction as more crash data is available from vehicle sensors. The third edition of this essential work includes a new chapter on the use of EDRs as well as examples using EDR data in accident reconstruction. Early chapters feature foundational material that is necessary for the understanding of vehicle collisions and vehicle motion; later chapters present applications of the methods and include example reconstructions. As a result, Vehicle Accident Analysis & Reconstruction Methods remains the definitive resource in accident reconstruction.

Multi-contrast CT imaging using a high energy resolution CdTe detector and a CZT photon-counting detector

Computed tomography (CT) imaging with high energy resolution detectors shows great promise in material decomposition and multi-contrast imaging. Multi-contrast imaging was studied by imaging a phantom with iodine (I), gadolinium (Gd), and gold (Au) solutions, and mixtures of the three using a cadmium telluride (CdTe) spectrometer with an energy resolution of 1% as well as with a cadmium zinc telluride (CZT) detector with an energy resolution of 13%. The phantom was imaged at 120 kVp and 1.1 mA with 7 mm of aluminum filtration. For the CdTe data collection, the phantom was imaged using a 0.2 mm diameter x-ray beam with 96 ten-second data acquisitions across the phantom at 45 rotation angles. For the CZT detector, we had 720 projections using a cone beam, and the six detector energy thresholds were set to 23, 33, 50, 64, 81, and 120 keV so that three thresholds corresponded to the K-edges of the contrast agents. Contrast agent isolation methods were then examined. K-edge subtraction and novel spectrometric algebraic image reconstruction (SAIR) were used for the CdTe data. K-edge subtraction alone was used for the CZT data. Linearity plots produced similar R 2 values and slopes for all three reconstruction methods. Comparing CdTe methods, SAIR offered less noise than CdTe K-edge subtraction and better geometric accuracy at low contrast concentrations. CdTe contrast agent images of I, Gd, and Au offered less noise and greater contrast than the CZT images, highlighting the benefits of high energy resolution CdTe detectors for possible use in pre-clinical or clinical CT imaging.

A comparison of spectral reconstruction methods applied to non-zero temperature NRQCD meson correlation functions

We present results from the fastsum collaboration’s programme to determine the spectrum of the bottomonium system as a function of temperature. Three different methods of extracting spectral information are discussed: a Maximum Likelihood approach using a Gaussian spectral function for the ground state, the Backus Gilbert method, and the Kernel Ridge Regression machine learning procedure. We employ the fastsum anisotropic lattices with 2+1 dynamical quark flavours, with temperatures ranging from 47 to 375 MeV.

Dose Reduction and Low-Contrast Detectability Using Iterative CBCT Reconstruction Algorithm for Radiotherapy

Introduction: Several studies have reported the relation between the imaging dose and secondary cancer risk and have emphasized the need to minimize the additional imaging dose as low as reasonably achievable. The iterative cone-beam computed tomography (iCBCT) algorithm can improve the image quality by utilizing scatter correction and statistical reconstruction. We investigate the use of a novel iCBCT reconstruction algorithm to reduce the patient dose while maintaining low-contrast detectability and registration accuracy. Methods: Catphan and anthropomorphic phantoms were analyzed. All CBCT images were acquired with varying dose levels and reconstructed with a Feldkamp–Davis–Kress algorithm-based CBCT (FDK-CBCT) and iCBCT. The low-contrast detectability was subjectively assessed using a 9-point scale by 4 reviewers and objectively assessed using structure similarity index (SSIM). The soft tissue-based registration error was analyzed for each dose level and reconstruction technique. Results: The results of subjective low-contrast detectability found that the iCBCT acquired at two-thirds of a dose was superior to the FDK-CBCT acquired at a full dose (6.4 vs 5.4). Relative to FDK-CBCT acquired at full dose, SSIM was higher for iCBCT acquired at one-sixth dose in head and head and neck region while equivalent with iCBCT acquired at two-thirds dose in pelvis region. The soft tissue-based registration was 2.2 and 0.6 mm for FDK-CBCT and iCBCT, respectively. Conclusion: Use of iCBCT reconstruction algorithm can generally reduce the patient dose by approximately two-thirds compared to conventional reconstruction methods while maintaining low-contrast detectability and accuracy of registration.

Post-Processing Ensemble Weather Forecasts for Introducing Multisite and Multivariable Correlations using Rank Shuffle and Copula Theory

Statistical methods have been widely used to post-process ensemble weather forecasts for hydrological predictions. However, most of the statistical post-processing methods apply to a single weather variable at a single location, thus neglecting the inter-site and inter-variable dependence structures of forecast variables. This study synthesized a multisite and multivariate (MSMV) post-processing framework that extends the univariate method to the MSMV version by directly rearranging the post-processed ensemble members (post-reordering strategy) or by rearranging the latent variables used in univariate method (pre-reordering strategy). Based on the univariate Generator-based Post-Processing (GPP) method, the two reordering strategies and three dependence reconstruction methods (Rank shuffle (RS), Gaussian Copula (GC), and Empirical Copula (EC)) totaling 6 MSMV methods (RS-Pre, GC-Pre, EC-Pre, RS-Post, GC-Post, and EC-Post) were evaluated in post-processing ensemble precipitation and temperature forecasts for the Xiangjiang Basin in China using the 11-member ensemble forecasts from the Global Ensemble Forecasting System (GEFS). The results showed that raw GEFS forecasts tend to be biased for both the forecast ensembles and the inter-site and inter-variable dependencies. Univariate method can improve the univariate performance of ensemble mean and spread but misrepresent the inter-site and inter-variable dependence among the forecast variables. The MSMV framework can well utilize the advantages of the univariate method and also reconstruct the inter-site and inter-variable dependencies. Among the six methods, RS-Pre, RS-Post, GC-Post, and EC-Post perform better than the others with respect to reproducing the univariate statistics and multivariable dependences. The post-reordering strategy is recommended to combine the univariate method (i.e. GPP) and reconstruction methods.