On the Determination of Coordination Numbers of Coupled DEM-DFN Model for Modeling Fractured Rocks

Natural fractured rocks usually contain background granular media and multi-scale fractures. The coordination number is a crucial factor to characterize the connection of microstructural elements. The determination of coordination numbers for modeling fractured rocks is essential to interpret the distribution of cracks related to micromechanical properties. We have built a consistent workflow of discrete element models (DEMs) coupled with discrete fracture networks (DFNs). This DEM-DFN model could provide a simple formulation for high calculation efficiency to model a more realistic and detailed description of fracture system. A series of numerical experiments are set up, aiming to correlate window radius, particle size, and crack length, which will benefit the window selection for measuring coordination numbers based on the crack characteristics. The coordination number determined in the DEM-DFN modeling can be used to identify crack patterns in the spatial distribution.

Automatic Focusing Method of Microscopes Based on Image Processing

Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.

Which particles to select, and if yes, how many?

Micro- and nanoplastic contamination is becoming a growing concern for environmental protection and food safety. Therefore, analytical techniques need to produce reliable quantification to ensure proper risk assessment. Raman microspectroscopy (RM) offers identification of single particles, but to ensure that the results are reliable, a certain number of particles has to be analyzed. For larger MP, all particles on the Raman filter can be detected, errors can be quantified, and the minimal sample size can be calculated easily by random sampling. In contrast, very small particles might not all be detected, demanding a window-based analysis of the filter. A bootstrap method is presented to provide an error quantification with confidence intervals from the available window data. In this context, different window selection schemes are evaluated and there is a clear recommendation to employ random (rather than systematically placed) window locations with many small rather than few larger windows. Ultimately, these results are united in a proposed RM measurement algorithm that computes confidence intervals on-the-fly during the analysis and, by checking whether given precision requirements are already met, automatically stops if an appropriate number of particles are identified, thus improving efficiency.

3D pointing gestures as target selection tools: guiding monocular UAVs during window selection in an outdoor environment

Firefighters need to gain information from both inside and outside of buildings in first response emergency scenarios. For this purpose, drones are beneficial. This paper presents an elicitation study that showed firefighters’ desires to collaborate with autonomous drones. We developed a Human–Drone interaction (HDI) method for indicating a target to a drone using 3D pointing gestures estimated solely from a monocular camera. The participant first points to a window without using any wearable or body-attached device. Through the drone’s front-facing camera, the drone detects the gesture and computes the target window. This work includes a description of the process for choosing the gesture, detecting and localizing objects, and carrying out the transformations between coordinate systems. Our proposed 3D pointing gesture interface improves on 2D interfaces by integrating depth information with SLAM and solving ambiguity with multiple objects aligned on the same plane in a large-scale outdoor environment. Experimental results showed that our 3D pointing gesture interface obtained average F1 scores of 0.85 and 0.73 for precision and recall in simulation and real-world experiments and an F1 score of 0.58 at the maximum distance of 25 m between the drone and building.

Energy Minimization Algorithm for Estimation of Clock Skew and Reception Window Selection in Wireless Networks

The synchronization of time between devices is one of the more important and challenging problems in wireless networks. We discuss the problem of maximization of the probability of receiving a message from a device using a limited listening time window to minimize energy utilization. We propose a solution to two important problems in wireless networks of battery-powered devices: a method of establishing a connection with a device that has been disconnected from the system for a long time and developed unknown skew and also two approaches to follow-up clock synchronization using the confidence interval method. We start with the analysis of measurements of clock skew. The algorithms are evaluated using extensive simulations and we discuss the selection of parameters balancing between minimizing the energy utilization and maximizing the probability of reception of the message. We show that the selection of a time window of growing size requires less energy to receive a packet than using the same size of time window repeated multiple times. The shifting of reception windows can further decrease the energy cost if lower packet reception probability is acceptable. We also propose and evaluate an algorithm scaling the reception window size to the interval between the packet transmission.

Towards 3D Multiscale Adjoint Waveform Tomography of the Lithosphere and Underlying Mantle beneath Southeast Asia

<p>Southeast Asia is one of the most complex tectonic regions on Earth. This is mainly a result of its location within the triple junction of the Australian, Eurasian and Philippine Sea plates which has created a complicated configuration of active plate tectonic boundaries. Adjoint waveform tomography is especially suitable for imaging such complex regions. By simulating the 3D wavefield, it is possible to directly compare observed and simulated seismograms, thereby taking into account both body and surface waves. The method can account for the effects of anisotropy, anelasticity, wavefront healing, interference and (de)focusing that can hamper other seismological methods.</p><p>To date, sparse instrument coverage in the region has contributed to a heterogeneous path coverage. In this project, we make use of publicly available data as well as our recently deployed networks of broadband seismometers on Borneo and Sulawesi. This, in addition to access to national permanent networks, provides data from over 300 stations which promises a significant improvement in data coverage around the Banda Arc, Borneo and Sulawesi. We employ a geographical weighting scheme to minimise the effect of dense regional arrays and compile a catalogue of 118 well-constrained earthquakes, optimising for coverage, signal-to-noise ratio and data availability. An optimised window selection algorithm allows us to balance amplitude differences and include as much signal as possible while avoiding noisy data.</p><p>Here, we present a seismic waveform tomography for upper mantle structure in Southeast Asia, imaging radially anisotropic S velocity, P velocity and density. We use a gradient-based optimisation scheme (L-BFGS) and adjoint methods to obtain sensitivity kernels as the corresponding gradients. In the first part of the inversion, periods down to 50 s are used to update a 1D initial model, adapting a multi-scale approach in which long periods are inverted for first to avoid cycle skipping. In our long-period results, we observe a strong regional low S-velocity structure with an underlying high-velocity anomaly. The results are consistent with the global <em>S40RTS</em> model. </p>

3D Pointing Gestures as Target Selection Tools: Guiding Monocular UAVs during Window Selection in an Outdoor Environment

Firefighters need to gain information from both inside and outside of buildings in first response emergency scenarios. For this purpose, drones are beneficial. This paper presents an elicitation study that showed the firefighters’ desire to collaborate with autonomous drones. We developed a Human-Drone Interaction (HDI) method for indicating a target to a drone using 3D pointing gestures estimated solely from a monocular camera. The participant first points to a window without using any wearable or body-attached device. Through the drone’s front-facing camera, the drone detects the gesture and computes the target window. This work includes a description of the process for choosing the gesture, detecting and localizing objects, and carrying out the transformations between coordinate systems. Our proposed 3D pointing gesture interface improves a 2D pointing gesture interface by integrating depth information with SLAM, solving multiple objects aligned on the same plane ambiguity, in a large-scale outdoor environment. Experimental results showed that our 3D pointing gesture interface obtained a 0.85 and 0.73 F1-Score on average in simulation and real-world experiments and 0.58 F1-Score at the maximum distance of 25 meters between drone and building.