Visual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds derived from these sources, evaluating their respective strengths and weaknesses. The DSM accurately captures terrain features and elevation changes, offering a comprehensive view of the land surface. Conversely, LiDAR point cloud data delivers detailed three-dimensional information, enabling precise mapping of terrain features and object detection. Our comparative analysis based on six selected locations with varied topographical arrangements considers factors such as visual acuity and computational efficiency. Additionally, we discuss the application of DSM and LiDAR point cloud data in view analysis, emphasizing their value in line-of-sight assessments and field operations. The results indicate greater precision of viewsheds created based on LiDAR point clouds. The analysis reveals that the greater precision in comparing differences between DSM and point LiDAR data ranges from 1.42% to 5.94%, while the results subtraction falls between 1.05% and 3.89% for the conditions analyzed, indicating a high degree of accuracy in the method. However, this process demands significant computational resources. It is best applied in limited areas, particularly in urban environments where such data is crucial for supporting research decisions.

As cars and other transportation devices become increasingly interconnected, mobility takes on a new meaning, offering new opportunities. The integration of new communications technologies in modern vehicles has generated an enormous variety of data from various communications sources. Hence, there is a demand for intelligent transportation systems that can provide safe and reliable transportation while maintaining environmental conditions such as pollution, CO2 emission, and energy consumption. This chapter provides an overview of the Intelligent Transportation Systems (ITS) models. Briefly, it discusses the most important features of the systems and challenges, mostly related to the security in data and information processing. Fast anomalies detection and prevention of external attacks may help solve the problems of traffic congestion and road safety to prevent accidents. The chapter contains the description of the realistic Smart Transportation System developed by the Wobcom company and implemented in Wolfsburg (Germany). That system is also used for practical validation of the security service components of the platform created in the GUARD project.

Variation of the bark depends on tree age, origin, geographic location, or site conditions like temperature and water availability. Most of these variables are characterized by very high variability but above of all are also affected by climate changes. This requires the construction of improved bark thickness models that take this complexity into account. We propose a new approach based on time series. We used a recurrent neural network (ANN) to build the bark thickness model and compare it with stem taper curves adjusted to predict double bark thickness. The data includes 750 felled trees from common oak and 144 Scots pine—trees representing dominant forest-forming tree species in Europe. The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built recurrent ANN and calculated bark thickness along the stem. We tested different network structures with one- and two-time window delay and three learning algorithms—Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate Gradient. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error as well as graphical analysis of observed and estimated values. The results show that recurrent ANN is a universal approach that offers the most precise estimation of bark thickness at a particular stem height. The ANN recursive model had an advantage in estimating trees that were atypical for height, as well as upper and lower parts on the stem.

Body Area Networks (BANs) connect together nodes attached to a human body and transfer the data to an external infrastructure. The wireless communication channel and a variety of miniature sensor devices have lead to many useful applications of BANs, such as healthcare monitoring, military and emergency coordination, rescue services, sports, and entertainment. The Ultra Wide Band (UWB) communication model is widely used in wireless body area networks. UWB Radio Frequency (RF) technology provides robust and energy efficient transmission of data and signals through wireless networks. This chapter surveys recent models, applications and research challenges for future generation UWB RF technology for BANs. The chapter also discusses the state-of-the art in the cloud-based support for data storage and analysis in mobile health monitoring. Security issues for BANs in general and mobile health monitoring are addressed as a key aspect of the recent developments in the domain.

Data integration and fast effective data processing are the primary challenges in today’s high-performance computing systems used for Big Data processing and analysis in practical scenarios. Blockchain (BC) is a hot, modern technology that ensures high security of data processes stored in highly distributed networks and ICT infrastructures. BC enables secure data transfers in distributed systems without the need for all operations and processes in the network to be initiated and monitored by any central authority (system manager). This paper presents the background of a generic architectural model of a BC system and explains the concept behind the consensus models used in BC transactions. Security is the main aspect of all defined operations and BC nodes. The paper presents also specific BC use cases to illustrate the performance of the system in practical scenarios..

This paper presents an innovative computer graphic method for viewshed generation from big point clouds. The proposed approach consists in simplification of typical methods for viewshed formation that are based on sorting and binary trees. The proposed method is based on the k-d tree concept optimized with radial segmentation and a dedicated mathematical algorithm for subtree rejection. The final visualization of the viewshed is designed with a graphic method using triangulated irregular network (TIN) surfaces from the accepted subtrees.