Testing the Effect of Bathymetric Data Reduction on the Shape of the Digital Bottom Model.

Depth data and the digital bottom model created from it are very important in the inland and coastal water zones studies and research. The paper undertakes the subject of bathymetric data processing using reduction methods and examines the impact of data reduction according to the resulting representations of the bottom surface in the form of numerical bottom models. Data reduction is an approach that is meant to reduce the size of the input dataset to make it easier and more efficient for analysis, transmission, storage and similar.

Comparative Analysis of Selected Geostatistical Methods for Bottom Surface Modeling.

Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets.

Quality of Life Assessment in Students from Polish Universities during the COVID-19 Pandemic According to WHO Quality of Life Questionnaire.

The outbreak of COVID-19 in December 2019 in China influenced the lives of people all over the world. Many had to face the completely new situation of lockdown. These changes influenced many aspects of life.

Sensors and Sensor's Fusion in Autonomous Vehicles.

Autonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications [...

Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data.

With the majority of research, in relation to 3D object reconstruction, focusing on single static synthetic object reconstruction, there is a need for a method capable of reconstructing morphing objects in dynamic scenes without external influence. However, such research requires a time-consuming creation of real world object ground truths. To solve this, we propose a novel three-staged deep adversarial neural network architecture capable of denoising and refining real-world depth sensor input for full human body posture reconstruction.

Automatic ship classification for a riverside monitoring system using a cascade of artificial intelligence techniques including penalties and rewards.

Riverside monitoring systems are used for controlling the passage of ships, counting them to prevent overcrowding in a port, or raising an alarm if the ship is unknown or not safe. This type of control and analysis is commonly carried out by many people who supervise CCTV in real time. In this paper, we present an alternative approach to automatic image analysis using a variety of artificial intelligence techniques.

Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data.

Floating autonomous vehicles are very often equipped with modern systems that collect information about the situation under the water surface, e.g., the depth or type of bottom and obstructions on the seafloor.

Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism.

The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames.

Automatic Classification Using Machine Learning for Non-Conventional Vessels on Inland Waters.

The prevalent methods for monitoring ships are based on automatic identification and radar systems. This applies mainly to large vessels. Additional sensors that are used include video cameras with different resolutions.