Aims. This paper introduces a kinetic code that simulates gamma-ray burst (GRB) afterglow emission from the external forward shock and presents examples of some of its applications. One interesting research topic discussed in the paper is the high-energy radiation produced by Compton scattering of the prompt GRB photons against the shock-accelerated electrons. The difference between the forward shock emission in a wind-type and a constant-density medium is also studied, and the emission due to Maxwellian electron injection is compared to the case with pure power-law electrons. Methods. The code calculates the time-evolving photon and electron distributions in the emission region by solving the relativistic kinetic equations for each particle species. For the first time, the full relativistic equations for synchrotron emission/absorption, Compton scattering, and pair production/annihilation were applied to model the forward shock emission. The synchrotron self-absorption thermalization mechanism, which shapes the low-energy end of the electron distribution, was also included in the electron equation. Results. The simulation results indicate that inverse Compton scattering of the prompt GRB photons can produce a luminous TeV emission component, even when pair production in the emission region is taken into account. This very high-energy radiation may be observable in low-redshift GRBs. The test simulations also show that the low-energy end of a pure power-law distribution of electrons can thermalize owing to synchrotron self-absorption in a wind-type environment, but without an observable impact on the radiation spectrum. Moreover, a flattening in the forward shock X-ray light curve may be expected when the electron injection function is assumed to be purely Maxwellian instead of a power law. The flux during such a flattening is likely to be lower than the Swift/XRT sensitivity in the case of a constant-density external medium, but a wind environment may result in a higher flux during the shallow decay.

This paper presents Kinetic Energy Difference (KED) as a metric for collision proximity. The calculation of KED for differentially driven robots is explained, along with an example obstacle avoidance algorithm that utilizes it. This example algorithm is computationally efficient and simulations show that it is capable of guiding robots with slow dynamics through narrow corridors.

Semantic segmentation directly from the images of landfills can be utilized in the earth movers to segregate the garbage autonomously. Generally, Various segregation methods are available for garbage segregation such as IOT based waste segregation, Conveyor belt segregation in which none of them are directly from landfills. Semantic segmentation is one of the important tasks that maps the path towards the complete scene understanding. The aim of this paper is to present a smart segregation method for garbage by using semantic segmentation with DeepLab V3+ Model using the framework(Backbone model) of Xception-65 with the mean accuracy of 75.01%. This paper features the segmentation with the GarbotV1 dataset which has major classifications such as Plastic, Cart-board, Wood, Metal, Sponge. The paper also contributes a method for reconstructing the segmented images to build a 3D map and this exploits the use of earth moving vehicles to navigate autonomously by localizing the segmented objects.

Lightweight, affordable spherical cameras can be utilized in mobile robotics to build a 3D map of the working environment of a robot. This paper demonstrates how to use the optical flow between two spherical images to quickly construct a semantically meaningful 3D model spanning all directions. The main contribution of the paper is the use of semantic segmentation to increase the robustness of the reconstruction method in both indoor and outdoor applications.