Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Learning Space-Time Continuous Neural PDEs from Partially Observed States

We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a space-time continuous latent neural PDE model with an efficient probabilistic framework and a novel encoder design for improved data efficiency and grid independence. The latent state dynamics are governed by a PDE model that combines the collocation method and the method of lines. We employ amortized variational inference for approximate posterior estimation and utilize a multiple shooting technique for enhanced training speed and stability. Our model demonstrates state-of-the-art performance on complex synthetic and real-world datasets, overcoming limitations of previous approaches and effectively handling partially-observed data. The proposed model outperforms recent methods, showing its potential to advance data-driven PDE modeling and enabling robust, grid-independent modeling of complex partially-observed dynamic processes

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

A Deep Learning Framework in Selected Remote Sensing Applications

The main research topic is designing and implementing a deep learning framework applied to remote sensing. Remote sensing techniques and applications play a crucial role in observing the Earth evolution, especially nowadays, where the effects of climate change on our life is more and more evident. A considerable amount of data are daily acquired all over the Earth. Effective exploitation of this information requires the robustness, velocity and accuracy of deep learning. This emerging need inspired the choice of this topic. The conducted studies mainly focus on two European Space Agency (ESA) missions: Sentinel 1 and Sentinel 2. Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their open access policy. The increasing interest gained by these satellites in the research laboratory and applicative scenarios pushed us to utilize them in the considered framework. The combined use of Sentinel 1 and Sentinel 2 is crucial and very prominent in different contexts and different kinds of monitoring when the growing (or changing) dynamics are very rapid. Starting from this general framework, two specific research activities were identified and investigated, leading to the results presented in this dissertation. Both these studies can be placed in the context of data fusion. The first activity deals with a super-resolution framework to improve Sentinel 2 bands supplied at 20 meters up to 10 meters. Increasing the spatial resolution of these bands is of great interest in many remote sensing applications, particularly in monitoring vegetation, rivers, forests, and so on. The second topic of the deep learning framework has been applied to the multispectral Normalized Difference Vegetation Index (NDVI) extraction, and the semantic segmentation obtained fusing Sentinel 1 and S2 data. The S1 SAR data is of great importance for the quantity of information extracted in the context of monitoring wetlands, rivers and forests, and many other contexts. In both cases, the problem was addressed with deep learning techniques, and in both cases, very lean architectures were used, demonstrating that even without the availability of computing power, it is possible to obtain high-level results. The core of this framework is a Convolutional Neural Network (CNN). {CNNs have been successfully applied to many image processing problems, like super-resolution, pansharpening, classification, and others, because of several advantages such as (i) the capability to approximate complex non-linear functions, (ii) the ease of training that allows to avoid time-consuming handcraft filter design, (iii) the parallel computational architecture. Even if a large amount of "labelled" data is required for training, the CNN performances pushed me to this architectural choice.} In our S1 and S2 integration task, we have faced and overcome the problem of manually labelled data with an approach based on integrating these two different sensors. Therefore, apart from the investigation in Sentinel-1 and Sentinel-2 integration, the main contribution in both cases of these works is, in particular, the possibility of designing a CNN-based solution that can be distinguished by its lightness from a computational point of view and consequent substantial saving of time compared to more complex deep learning state-of-the-art solutions

A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

Design Framework of UAV-Based Environment Sensing, Localization, and Imaging System

In this dissertation research, we develop a framework for designing an Unmanned Aerial Vehicle or UAV-based environment sensing, localization, and imaging system for challenging environments with no GPS signals and low visibility. The UAV system relies on the various sensors that it carries to conduct accurate sensing and localization of the objects in an environment, and further to reconstruct the 3D shapes of those objects. The system can be very useful when exploring an unknown or dangerous environment, e.g., a disaster site, which is not convenient or not accessible for humans. In addition, the system can be used for monitoring and object tracking in a large scale environment, e.g., a smart manufacturing factory, for the purposes of workplace management/safety, and maintaining optimal system performance/productivity. In our framework, the UAV system is comprised of two subsystems: a sensing and localization subsystem; and a mmWave radar-based 3D object reconstruction subsystem. The first subsystem is referred to as LIDAUS (Localization of IoT Device via Anchor UAV SLAM), which is an infrastructure-free, multi-stage SLAM (Simultaneous Localization and Mapping) system that utilizes a UAV to accurately localize and track IoT devices in a space with weak or no GPS signals. The rapidly increasing deployment of Internet of Things (IoT) around the world is changing many aspects of our society. IoT devices can be deployed in various places for different purposes, e.g., in a manufacturing site or a large warehouse, and they can be displaced over time due to human activities, or manufacturing processes. Usually in an indoor environment, the lack of GPS signals and infrastructure support makes most existing indoor localization systems not practical when localizing a large number of wireless IoT devices. In addition, safety concerns, access restriction, and simply the huge amount of IoT devices make it not practical for humans to manually localize and track IoT devices. Our LIDAUS is developed to address these problems. The UAV in our LIDAUS system conducts multi-stage 3D SLAM trips to localize devices based only on Received Signal Strength Indicator (RSSI), the most widely available measurement of the signals of almost all commodity IoT devices. Our simulations and experiments of Bluetooth IoT devices demonstrate that our system LIDAUS can achieve high localization accuracy based only on RSSIs of commodity IoT devices. Build on the first subsystem, we further develop the second subsystem for environment reconstruction and imaging via mmWave radar and deep learning. This subsystem is referred to as 3DRIMR/R2P (3D Reconstruction and Imaging via mmWave Radar/Radar to Point Cloud). It enables an exploring UAV to fly within an environment and collect mmWave radar data by scanning various objects in the environment. Taking advantage of the accurate locations given by the first subsystem, the UAV can scan an object from different viewpoints. Then based on radar data only, the UAV can reconstruct the 3D shapes of the objects in the space. mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However, tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, large noise, and multi-path induced shadow reflections and artifacts. Hence, it is challenging to reconstruct 3D object shapes based on the raw sparse and low-resolution mmWave radar signals. To address the challenges, our second subsystem utilizes deep learning models to extract features from sparse raw mmWave radar intensity data, and reconstructs 3D shapes of objects in the format of dense and detailed point cloud. We first develop a deep learning model to reconstruct a single object’s 3D shape. The model first converts mmWave radar data to depth images, and then reconstructs an object’s 3D shape in point cloud format. Our experiments demonstrate the significant performance improvement of our system over the popular existing methods such as PointNet, PointNet++ and PCN. Then we further explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. We evaluate two different models. Model 1 is 3DRIMR/R2P model, and Model 2 is formed by adding a segmentation stage in the processing pipeline of Model 1. Our experiments demonstrate that both models are promising in solving the multiple object reconstruction problem. We also show that Model 2, despite producing denser and smoother point clouds, can lead to higher reconstruction loss or even missing objects. In addition, we find that both models are robust to the highly noisy radar data obtained by unstable Synthetic Aperture Radar (SAR) operation due to the instability or vibration of a small UAV hovering at its intended scanning point. Our research shows a promising direction of applying mmWave radar sensing in 3D object reconstruction

A Novel Computer Vision-Based Framework For Supervised Classification Of Energy Outbreak Phenomena

Today, there is a need to implement a proper design of an adequate surveillance system that detects and categorizes explosion phenomena in order to identify the explosion risk to reduce its impact through mitigation and preparedness. This dissertation introduces state-of-the-art classification of explosion phenomena through pattern recognition techniques on color images. Consequently, we present a novel taxonomy for explosion phenomena. In particular, we demonstrate different aspects of volcanic eruptions and nuclear explosions of the proposed taxonomy that include scientific formation, real examples, existing monitoring methodologies, and their limitations. In addition, we propose a novel framework designed to categorize explosion phenomena against non-explosion phenomena. Moreover, a new dataset, Volcanic and Nuclear Explosions (VNEX), was collected. The totality of VNEX is 10, 654 samples, and it includes the following patterns: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and sky clouds. In order to achieve high reliability in the proposed explosion classification framework, we propose to employ various feature extraction approaches. Thus, we calculated the intensity levels to extract the texture features. Moreover, we utilize the YCbCr color model to calculate the amplitude features. We also employ the Radix-2 Fast Fourier Transform to compute the frequency features. Furthermore, we use the uniform local binary patterns technique to compute the histogram features. Additionally, these discriminative features were combined into a single input vector that provides valuable insight of the images, and then fed into the following classification techniques: Euclidian distance, correlation, k-nearest neighbors, one-against-one multiclass support vector machines with different kernels, and the multilayer perceptron model. Evaluation results show the design of the proposed framework is effective and robust. Furthermore, a trade-off between the computation time and the classification rate was achieved

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

'THz Torch' wireless communications links

The low-cost 'THz Torch’ technology, which exploits the thermal infrared spectrum (ca. 10 to 100 THz), was recently introduced to provide secure low data rate communications links across short ranges. In this thesis, the channel model for 'THz Torch’ wireless communications links is redeveloped from a thermodynamics perspective. Novel optimization-based channel estimators are also proposed to calibrate parameters in the channel model. Based on these theoretical advances, a cognitive 'THz Torch’ receiver, which combines conventional digital communications with state-of-the-art deep learning techniques, is presented to achieve cognitive synchronization and demodulation. The newly reported 'THz Torch’ wireless link is capable of bypassing the thermal time constant constraints normally associated with both the thermal emitter and sensor, allowing truly asynchronous data transfer with direct electronic modulation. Experimental results obtained in both laboratory environments and field trials demonstrate step-change improvements in channel range, bit rate, bit error rate and demodulation speed. This work represents a paradigm shift in modulation-demodulation with a thermal-based physical layer and offers a practical solution for implementing future ubiquitous secure 'THz Torch’ wireless communications links. The cognitive receiver concept also has wide-ranging implications for future communications and sensor technologies, making them more resilient when operating in harsh environments.Open Acces

Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization