This research article presents the utilization of an adaptive camera system for virtual teleport in videoconferencing, enabling users to experience remote locations in an immersive and interactive manner. The camera system incorporates features such as motion of camera system in X and Y axes, object tracking, and detection using Ultralytics YOLO V5 (You Only Look Once). The live video feed from the camera system is streamed over the internet by using OBS (Open Broadcaster Software version 29.1.3) virtual camera via platforms like Google Meet and viewed through VR headsets. Users can control the camera's movement and direction using intuitive interfaces like joysticks, enhancing their sense of presence and immersion. This article highlights the design and implementation of the adaptive camera system, its integration with virtual reality technologies, and the user experience it offers. The findings demonstrate the potential of this system for providing a realistic and engaging virtual teleportation experience in future.
In this paper we explore the possibilities of using Neural Networks with transfer learning approach and Dynamic Time Warping for online signature verification. Verification process of proposed methods is based on SVC-2004 signature database, which contains genuine and skilled forgery signatures. Experimental results show that approach using Convolutional Neural Networks is more successful and this method is achieving an Equal Error Rate (EER) as low as 2.25% using 10-fold cross-validation. These results are competitive with other published works.
As part of the ‘Zero Emission Sail Ship’ project, one of the research tasks was to develop an automatic sailing system. To achieve this, we created a mathematical model of the steering system for the three-mast schooner Klara, which has the same hull as the future zero-emission sailing ship. The paper describes the identification procedure for both linear and nonlinear steering ship models, based on several sea trials with the sailing ship Klara. The best of these models will be used in automatic sailing system simulation.
A new CMOS active resistor structure is submitted. This new proposed symmetrical structure offers the possibility to obtain a strong improvement of the main circuit performances, as compared to the classical similar designs. This circuit current-voltage characteristic is very linear and the domain of the differential input voltage allowed by the circuit is very large, for obtaining this excellent linearity. As a result of the biasing in saturation of all MOS active devices, frequency response of the new proposed structure will be strongly increased. This original circuit allows a very important diminution of the circuit area as compared to physical resistors, accurately simulating the Ohm law for the equivalent resistances up to megaohms. The SPICE simulations validate the theoretical approach, showing a very good linearity, a large domain of the input voltage also a very good frequency response.
Detecting brain aneurysms is a critical area of research due to the potential life-threatening consequences of aneurysm rupture. Early detection of a cerebral (brain) aneurysm can increase the chances of successful treatment. In this paper, we explore various neural network architectures such as 2DCNN, PointCNN, PointNet and 3DCNN for classification of cerebral aneurysms. Our main objective is to develop a reliable system that enhances the accuracy of aneurysm detection by medical professionals. Ultimately, the goal is to contribute towards improving patient outcomes and saving lives. Our experimental results, which were obtained using the IntrA: 3D Intracranial Aneurysm dataset, indicate the superiority of the proposed 2DCNN and 3DCNN for detection of brain aneurysms. We compare the results with other deep learning architectures. Our proposed 2DCNN and 3DCNN architectures achieved precision of 85.91% and 89.05%, respectively.
